Show of Hands:Votes: Party classification:: Votes_RFEXNOr_cnk05_rest
bdanalytics
Date: (Fri) Jun 10, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
# glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbRFESizes[["RFE.X"]] <- c(96, 112, 120, 124, 128, 129, 130, 131, 132, 133, 135, 138, 142, 157, 187, 247) # accuracy(131) = 0.6285
glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
, "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
, "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
, "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
, "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
, "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
, "svmRadial" # didn't bother
,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
))
}
glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
glbMdlAllowParallel[["All.X#zv.pca#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X#ica#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["RFE.X#zv.pca#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["RFE.X#ica#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["RFE.X#nzv.pca.spatialSign#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#glm"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#bayesglm"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#rpart"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#lda"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#lda2"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#svmLinear"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#svmPoly"]] <- FALSE
glbMdlAllowParallel[["RFE.X##rcv#earth"]] <- FALSE
glbMdlAllowParallel[["Final.RFE.X#zv.pca#rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list( # NULL # : default
"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
c("knnImpute", "bagImpute", "medianImpute")),
# NULL))
c("nzv.spatialSign")))
)
glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
"nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Votes_RFEXNOr_cnk05_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "fit.models_1" #default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # NULL #default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Votes_RFEXNOr_cnk05_fit.models_1_fit.models_1.RData" # "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1 1 0 0 6.813 NA NAStep 1.0: fit models_1
chunk option: eval=
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 230.13 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 230.130 230.143
## 2 fit.models_1_All.X 1 1 setup 230.143 NA
## elapsed
## 1 0.013
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 230.143 230.15
## 3 fit.models_1_All.X 1 2 glmnet 230.150 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "skipping fitting model: All.X##rcv#glmnet"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 230.150 230.155
## 4 fit.models_1_All.X 1 3 glm 230.155 NA
## elapsed
## 3 0.005
## 4 NA
## [1] "skipping fitting model: All.X##rcv#glm"
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 230.155 230.16
## 5 fit.models_1_RFE.X 1 4 setup 230.160 NA
## elapsed
## 4 0.005
## 5 NA
## label step_major step_minor label_minor bgn end
## 5 fit.models_1_RFE.X 1 4 setup 230.160 230.167
## 6 fit.models_1_RFE.X 1 5 bagEarth 230.167 NA
## elapsed
## 5 0.007
## 6 NA
## [1] "skipping fitting model: RFE.X##rcv#bagEarth"
## label step_major step_minor label_minor bgn end
## 6 fit.models_1_RFE.X 1 5 bagEarth 230.167 230.174
## 7 fit.models_1_RFE.X 1 6 glm 230.175 NA
## elapsed
## 6 0.008
## 7 NA
## [1] "skipping fitting model: RFE.X##rcv#glm"
## label step_major step_minor label_minor bgn end
## 7 fit.models_1_RFE.X 1 6 glm 230.175 230.18
## 8 fit.models_1_RFE.X 1 7 glmnet 230.180 NA
## elapsed
## 7 0.005
## 8 NA
## [1] "skipping fitting model: RFE.X##rcv#glmnet"
## label step_major step_minor label_minor bgn end
## 8 fit.models_1_RFE.X 1 7 glmnet 230.180 230.185
## 9 fit.models_1_RFE.X 1 8 nnet 230.185 NA
## elapsed
## 8 0.005
## 9 NA
## [1] "skipping fitting model: RFE.X##rcv#nnet"
## label step_major step_minor label_minor bgn end
## 9 fit.models_1_RFE.X 1 8 nnet 230.185 230.19
## 10 fit.models_1_RFE.X 1 9 rpart 230.190 NA
## elapsed
## 9 0.005
## 10 NA
## [1] "skipping fitting model: RFE.X##rcv#rpart"
## label step_major step_minor label_minor bgn end
## 10 fit.models_1_RFE.X 1 9 rpart 230.190 230.195
## 11 fit.models_1_RFE.X 1 10 gbm 230.195 NA
## elapsed
## 10 0.005
## 11 NA
## [1] "skipping fitting model: RFE.X##rcv#gbm"
## label step_major step_minor label_minor bgn end
## 11 fit.models_1_RFE.X 1 10 gbm 230.195 230.2
## 12 fit.models_1_RFE.X 1 11 avNNet 230.200 NA
## elapsed
## 11 0.005
## 12 NA
## [1] "skipping fitting model: RFE.X##rcv#avNNet"
## label step_major step_minor label_minor bgn end
## 12 fit.models_1_RFE.X 1 11 avNNet 230.200 230.205
## 13 fit.models_1_RFE.X 1 12 rf 230.205 NA
## elapsed
## 12 0.005
## 13 NA
## [1] "skipping fitting model: RFE.X##rcv#rf"
## label step_major step_minor label_minor bgn end
## 13 fit.models_1_RFE.X 1 12 rf 230.205 230.211
## 14 fit.models_1_RFE.X 1 13 earth 230.212 NA
## elapsed
## 13 0.006
## 14 NA
## [1] "skipping fitting model: RFE.X##rcv#earth"
## label step_major step_minor label_minor bgn
## 14 fit.models_1_RFE.X 1 13 earth 230.212
## 15 fit.models_1_All.X.Inc 1 14 setup 230.219
## end elapsed
## 14 230.218 0.006
## 15 NA NA## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## label step_major step_minor label_minor bgn
## 15 fit.models_1_All.X.Inc 1 14 setup 230.219
## 16 fit.models_1_All.X.Inc 1 15 glmnet 230.529
## end elapsed
## 15 230.528 0.309
## 16 NA NA
## [1] "skipping fitting model: All.X.Inc#nzv.spatialSign#rcv#glmnet"
## label step_major step_minor label_minor bgn
## 16 fit.models_1_All.X.Inc 1 15 glmnet 230.529
## 17 fit.models_1_RFE.X.Inc 1 16 setup 230.536
## end elapsed
## 16 230.535 0.006
## 17 NA NA## Loading required package: earth## Loading required package: plotmo## Loading required package: plotrix## Loading required package: TeachingDemos## label step_major step_minor label_minor bgn
## 17 fit.models_1_RFE.X.Inc 1 16 setup 230.536
## 18 fit.models_1_RFE.X.Inc 1 17 bagEarth 260.183
## end elapsed
## 17 260.182 29.647
## 18 NA NA
## [1] "skipping fitting model: RFE.X.Inc##rcv#bagEarth"## label step_major step_minor label_minor bgn
## 18 fit.models_1_RFE.X.Inc 1 17 bagEarth 260.183
## 19 fit.models_1_preProc 1 18 preProc 260.190
## end elapsed
## 18 260.189 0.006
## 19 NA NA## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## id
## RFE.X.Inc##rcv#bagEarth RFE.X.Inc##rcv#bagEarth
## RFE.X##rcv#bagEarth RFE.X##rcv#bagEarth
## RFE.X#zv.pca#rcv#glmnet RFE.X#zv.pca#rcv#glmnet
## All.X#nzv.spatialSign#rcv#glmnet All.X#nzv.spatialSign#rcv#glmnet
## All.X#spatialSign#rcv#glmnet All.X#spatialSign#rcv#glmnet
## feats
## RFE.X.Inc##rcv#bagEarth Q121011.fctr,Gender.fctr,Q100010.fctr,Q100562.fctr,Q100680.fctr,Q101162.fctr,Q101596.fctr,Q102089.fctr,Q102289.fctr,Q102687.fctr,Q102906.fctr,Q103293.fctr,Q105655.fctr,Q105840.fctr,Q106042.fctr,Q106272.fctr,Q106388.fctr,Q106389.fctr,Q106993.fctr,Q107491.fctr,Q107869.fctr,Q108617.fctr,Q108754.fctr,Q108855.fctr,Q108856.fctr,Q108950.fctr,Q109367.fctr,Q110740.fctr,Q111220.fctr,Q111580.fctr,Q111848.fctr,Q112270.fctr,Q112478.fctr,Q112512.fctr,Q113583.fctr,Q113584.fctr,Q113992.fctr,Q114152.fctr,Q114386.fctr,Q114517.fctr,Q114748.fctr,Q114961.fctr,Q115195.fctr,Q115390.fctr,Q115602.fctr,Q115610.fctr,Q115777.fctr,Q115899.fctr,Q116197.fctr,Q116441.fctr,Q116448.fctr,Q116601.fctr,Q116797.fctr,Q116953.fctr,Q117186.fctr,Q117193.fctr,Q118117.fctr,Q118232.fctr,Q118233.fctr,Q118237.fctr,Q118892.fctr,Q119851.fctr,Q120012.fctr,Q120014.fctr,Q120194.fctr,Q120472.fctr,Q120650.fctr,Q120978.fctr,Q121700.fctr,Q122120.fctr,Q122769.fctr,Q122770.fctr,Q123464.fctr,Q124742.fctr,Q96024.fctr,Q98078.fctr,Q98578.fctr,Q98869.fctr,Q99480.fctr,Q99581.fctr,Q99716.fctr,Q99982.fctr,Q109244.fctr*Q98197.fctr,Q109244.fctr*Q115611.fctr,Q109244.fctr*Q116881.fctr,Q109244.fctr*Q113181.fctr,Q109244.fctr*Hhold.fctr,Q109244.fctr*Edn.fctr,Q109244.fctr*Q108342.fctr,Q109244.fctr*Q106997.fctr,Q109244.fctr*Q100689.fctr,Q109244.fctr*Q119334.fctr,Q109244.fctr*Q122771.fctr,Q109244.fctr*Q119650.fctr,Q109244.fctr*Q124122.fctr,Q109244.fctr*Income.fctr,Q109244.fctr*Q123621.fctr,Q109244.fctr*Q121699.fctr,Q109244.fctr*Q102674.fctr,Q109244.fctr*Q108343.fctr,Q109244.fctr*Q120379.fctr,Q109244.fctr*Q101163.fctr,Q109244.fctr*Q104996.fctr,Q109244.fctr*YOB.Age.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## RFE.X##rcv#bagEarth Q109244.fctr,Q115611.fctr,Q113181.fctr,Gender.fctr,Q98197.fctr,Q101163.fctr,Q120379.fctr,Hhold.fctr,Q105840.fctr,Q120472.fctr,Q106272.fctr,Q115899.fctr,Q119851.fctr,Q99480.fctr,Q106042.fctr,Q98869.fctr,Q101596.fctr,Q110740.fctr,Q102089.fctr,Q120014.fctr,Q120650.fctr,Q108855.fctr,Edn.fctr,Q118892.fctr,Q116881.fctr,Q100680.fctr,Q108342.fctr,Q107869.fctr,Q112478.fctr,Q115195.fctr,Q121699.fctr,Q106388.fctr,Q106389.fctr,Q120012.fctr,Q116448.fctr,Q119650.fctr,Q96024.fctr,Q118237.fctr,Income.fctr,Q123621.fctr,Q108617.fctr,Q113583.fctr,Q118232.fctr,Q120194.fctr,Q122770.fctr,Q121700.fctr,Q124122.fctr,Q114152.fctr,Q122771.fctr,Q106997.fctr,Q98078.fctr,Q112270.fctr,Q116953.fctr,Q111848.fctr,Q100689.fctr,Q118233.fctr,Q99982.fctr,Q106993.fctr,Q115390.fctr,Q102906.fctr,Q123464.fctr,Q116797.fctr,Q104996.fctr,Q109367.fctr,Q111220.fctr,Q99716.fctr,Q117186.fctr,Q102674.fctr,Q118117.fctr,Q122769.fctr,Q108754.fctr,Q121011.fctr,Q116441.fctr,YOB.Age.fctr,Q105655.fctr,Q108856.fctr,Q120978.fctr,Q101162.fctr,Q119334.fctr,Q102289.fctr,Q111580.fctr,Q112512.fctr,Q99581.fctr,Q122120.fctr,Q116197.fctr,Q114386.fctr,Q117193.fctr,Q114961.fctr,Q115777.fctr,Q100010.fctr,Q113584.fctr,Q98578.fctr,Q102687.fctr,Q100562.fctr,Q116601.fctr,Q103293.fctr,Q108950.fctr,Q115602.fctr,Q107491.fctr,Q114748.fctr,Q114517.fctr,Q115610.fctr,Q124742.fctr,Q108343.fctr,Q113992.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## RFE.X#zv.pca#rcv#glmnet Q109244.fctr,Q115611.fctr,Q113181.fctr,Gender.fctr,Q98197.fctr,Q101163.fctr,Q120379.fctr,Hhold.fctr,Q105840.fctr,Q120472.fctr,Q106272.fctr,Q115899.fctr,Q119851.fctr,Q99480.fctr,Q106042.fctr,Q98869.fctr,Q101596.fctr,Q110740.fctr,Q102089.fctr,Q120014.fctr,Q120650.fctr,Q108855.fctr,Edn.fctr,Q118892.fctr,Q116881.fctr,Q100680.fctr,Q108342.fctr,Q107869.fctr,Q112478.fctr,Q115195.fctr,Q121699.fctr,Q106388.fctr,Q106389.fctr,Q120012.fctr,Q116448.fctr,Q119650.fctr,Q96024.fctr,Q118237.fctr,Income.fctr,Q123621.fctr,Q108617.fctr,Q113583.fctr,Q118232.fctr,Q120194.fctr,Q122770.fctr,Q121700.fctr,Q124122.fctr,Q114152.fctr,Q122771.fctr,Q106997.fctr,Q98078.fctr,Q112270.fctr,Q116953.fctr,Q111848.fctr,Q100689.fctr,Q118233.fctr,Q99982.fctr,Q106993.fctr,Q115390.fctr,Q102906.fctr,Q123464.fctr,Q116797.fctr,Q104996.fctr,Q109367.fctr,Q111220.fctr,Q99716.fctr,Q117186.fctr,Q102674.fctr,Q118117.fctr,Q122769.fctr,Q108754.fctr,Q121011.fctr,Q116441.fctr,YOB.Age.fctr,Q105655.fctr,Q108856.fctr,Q120978.fctr,Q101162.fctr,Q119334.fctr,Q102289.fctr,Q111580.fctr,Q112512.fctr,Q99581.fctr,Q122120.fctr,Q116197.fctr,Q114386.fctr,Q117193.fctr,Q114961.fctr,Q115777.fctr,Q100010.fctr,Q113584.fctr,Q98578.fctr,Q102687.fctr,Q100562.fctr,Q116601.fctr,Q103293.fctr,Q108950.fctr,Q115602.fctr,Q107491.fctr,Q114748.fctr,Q114517.fctr,Q115610.fctr,Q124742.fctr,Q108343.fctr,Q113992.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X#nzv.spatialSign#rcv#glmnet Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X#spatialSign#rcv#glmnet Gender.fctr,Q115611.fctr,Q113181.fctr,Q98197.fctr,Q120472.fctr,Q116881.fctr,Q101596.fctr,Q106272.fctr,Q110740.fctr,Q108855.fctr,Q122771.fctr,Q99480.fctr,Q106388.fctr,Q115899.fctr,Q120014.fctr,Q107869.fctr,Q98869.fctr,Q120650.fctr,Q122769.fctr,Q123621.fctr,Q118117.fctr,Q116441.fctr,Q122120.fctr,Q119334.fctr,Q106993.fctr,Q105655.fctr,Q117186.fctr,Q122770.fctr,Q114152.fctr,Q120194.fctr,Q118232.fctr,Income.fctr,Q116197.fctr,Q102289.fctr,Q118233.fctr,Q108856.fctr,Q99982.fctr,Q117193.fctr,Q123464.fctr,Q111580.fctr,Q119650.fctr,Q118237.fctr,Q112270.fctr,Q116797.fctr,Q124742.fctr,Q99581.fctr,Q115777.fctr,Q101162.fctr,Q98578.fctr,Q108754.fctr,.rnorm,Q106389.fctr,Q96024.fctr,Q108343.fctr,Q112512.fctr,Q120978.fctr,Q106997.fctr,Q115610.fctr,Q116953.fctr,Q115602.fctr,Q100010.fctr,Q108617.fctr,Q100562.fctr,Q107491.fctr,Q114748.fctr,Q112478.fctr,Q103293.fctr,Q102674.fctr,Q108950.fctr,Q113584.fctr,Q102906.fctr,Q104996.fctr,Q116601.fctr,Q116448.fctr,Q106042.fctr,Q121011.fctr,Q113992.fctr,Q111220.fctr,Q124122.fctr,Q121700.fctr,Q114961.fctr,Q109367.fctr,Q120012.fctr,Q114517.fctr,Q119851.fctr,Q115390.fctr,Q102687.fctr,Q118892.fctr,YOB.Age.fctr,Q111848.fctr,Q108342.fctr,Q100680.fctr,Q114386.fctr,Q98059.fctr,Q102089.fctr,Q115195.fctr,Q113583.fctr,Q105840.fctr,Q121699.fctr,Q120379.fctr,Q99716.fctr,Q98078.fctr,Q100689.fctr,Q101163.fctr,Edn.fctr,Hhold.fctr,Q109244.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns
## RFE.X.Inc##rcv#bagEarth 1
## RFE.X##rcv#bagEarth 1
## RFE.X#zv.pca#rcv#glmnet 25
## All.X#nzv.spatialSign#rcv#glmnet 25
## All.X#spatialSign#rcv#glmnet 25
## min.elapsedtime.everything
## RFE.X.Inc##rcv#bagEarth 1752.739
## RFE.X##rcv#bagEarth 884.423
## RFE.X#zv.pca#rcv#glmnet 74.676
## All.X#nzv.spatialSign#rcv#glmnet 52.416
## All.X#spatialSign#rcv#glmnet 36.885
## min.elapsedtime.final max.AUCpROC.fit
## RFE.X.Inc##rcv#bagEarth 952.985 0.6826181
## RFE.X##rcv#bagEarth 433.705 0.6752439
## RFE.X#zv.pca#rcv#glmnet 1.900 0.6647597
## All.X#nzv.spatialSign#rcv#glmnet 4.971 0.6706561
## All.X#spatialSign#rcv#glmnet 3.342 0.6612800
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## RFE.X.Inc##rcv#bagEarth 0.6987288 0.6665074 0.7525082
## RFE.X##rcv#bagEarth 0.6944915 0.6559962 0.7483972
## RFE.X#zv.pca#rcv#glmnet 0.6940678 0.6354515 0.7353638
## All.X#nzv.spatialSign#rcv#glmnet 0.7139831 0.6273292 0.7404593
## All.X#spatialSign#rcv#glmnet 0.6961864 0.6263736 0.7331124
## opt.prob.threshold.fit max.f.score.fit
## RFE.X.Inc##rcv#bagEarth 0.5 0.6644439
## RFE.X##rcv#bagEarth 0.5 0.6558395
## RFE.X#zv.pca#rcv#glmnet 0.5 0.6417370
## All.X#nzv.spatialSign#rcv#glmnet 0.5 0.6434697
## All.X#spatialSign#rcv#glmnet 0.5 0.6362533
## max.Accuracy.fit max.AccuracyLower.fit
## RFE.X.Inc##rcv#bagEarth 0.6169886 0.6696948
## RFE.X##rcv#bagEarth 0.6288811 0.6624320
## RFE.X#zv.pca#rcv#glmnet 0.6459313 0.6524516
## All.X#nzv.spatialSign#rcv#glmnet 0.6478785 0.6592557
## All.X#spatialSign#rcv#glmnet 0.6492270 0.6492775
## max.AccuracyUpper.fit max.Kappa.fit
## RFE.X.Inc##rcv#bagEarth 0.6972322 0.2304317
## RFE.X##rcv#bagEarth 0.6901322 0.2556984
## RFE.X#zv.pca#rcv#glmnet 0.6803636 0.2875399
## All.X#nzv.spatialSign#rcv#glmnet 0.6870247 0.2918534
## All.X#spatialSign#rcv#glmnet 0.6772539 0.2959061
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## RFE.X.Inc##rcv#bagEarth 0.5671007 0.5922166 0.5419847
## RFE.X##rcv#bagEarth 0.5601161 0.5820643 0.5381679
## RFE.X#zv.pca#rcv#glmnet 0.5792308 0.6412860 0.5171756
## All.X#nzv.spatialSign#rcv#glmnet 0.5572148 0.6125212 0.5019084
## All.X#spatialSign#rcv#glmnet 0.5570372 0.5854484 0.5286260
## max.AUCROCR.OOB opt.prob.threshold.OOB
## RFE.X.Inc##rcv#bagEarth 0.5840728 0.6
## RFE.X##rcv#bagEarth 0.5829523 0.6
## RFE.X#zv.pca#rcv#glmnet 0.5816510 0.5
## All.X#nzv.spatialSign#rcv#glmnet 0.5787416 0.6
## All.X#spatialSign#rcv#glmnet 0.5793099 0.6
## max.f.score.OOB max.Accuracy.OOB
## RFE.X.Inc##rcv#bagEarth 0.4200000 0.5838565
## RFE.X##rcv#bagEarth 0.4243176 0.5838565
## RFE.X#zv.pca#rcv#glmnet 0.5382324 0.5829596
## All.X#nzv.spatialSign#rcv#glmnet 0.3702703 0.5820628
## All.X#spatialSign#rcv#glmnet 0.3895425 0.5811659
## max.AccuracyLower.OOB
## RFE.X.Inc##rcv#bagEarth 0.5542859
## RFE.X##rcv#bagEarth 0.5542859
## RFE.X#zv.pca#rcv#glmnet 0.5533825
## All.X#nzv.spatialSign#rcv#glmnet 0.5524792
## All.X#spatialSign#rcv#glmnet 0.5515760
## max.AccuracyUpper.OOB max.Kappa.OOB
## RFE.X.Inc##rcv#bagEarth 0.6129835 0.1416702
## RFE.X##rcv#bagEarth 0.6129835 0.1422423
## RFE.X#zv.pca#rcv#glmnet 0.6120979 0.1591675
## All.X#nzv.spatialSign#rcv#glmnet 0.6112122 0.1321827
## All.X#spatialSign#rcv#glmnet 0.6103264 0.1327466
## max.AccuracySD.fit max.KappaSD.fit
## RFE.X.Inc##rcv#bagEarth NA NA
## RFE.X##rcv#bagEarth NA NA
## RFE.X#zv.pca#rcv#glmnet 0.006586809 0.01412354
## All.X#nzv.spatialSign#rcv#glmnet 0.007992229 0.01690955
## All.X#spatialSign#rcv#glmnet 0.008065080 0.01720582## min.elapsedtime.everything
## Random###myrandom_classfr 0.265
## MFO###myMFO_classfr 0.399
## Max.cor.Y.rcv.1X1###glmnet 0.753
## Max.cor.Y##rcv#rpart 1.624
## Interact.High.cor.Y##rcv#glmnet 5.284
## RFE.X##rcv#glm 12.435
## All.X##rcv#glm 14.312
## RFE.X##rcv#rpart 15.749
## RFE.X##rcv#glmnet 21.949
## All.X#conditionalX#rcv#glmnet 22.580
## All.X##rcv#glmnet 22.609
## Low.cor.X##rcv#glmnet 22.775
## RFE.X#conditionalX#rcv#glmnet 23.022
## All.X#zv#rcv#glmnet 23.568
## RFE.X#zv#rcv#glmnet 24.269
## RFE.X#scale#rcv#glmnet 25.649
## RFE.X#center#rcv#glmnet 25.755
## All.X#scale#rcv#glmnet 26.279
## All.X#center#rcv#glmnet 26.891
## RFE.X#range#rcv#glmnet 27.116
## RFE.X#BoxCox#rcv#glmnet 28.258
## All.X#range#rcv#glmnet 28.776
## All.X#BoxCox#rcv#glmnet 29.405
## RFE.X#center.scale#rcv#glmnet 29.657
## All.X#center.scale#rcv#glmnet 30.287
## RFE.X#nzv#rcv#glmnet 34.109
## All.X#nzv#rcv#glmnet 36.115
## All.X#spatialSign#rcv#glmnet 36.885
## RFE.X#ica#rcv#glmnet 39.908
## All.X#ica#rcv#glmnet 41.240
## RFE.X#spatialSign#rcv#glmnet 46.699
## RFE.X#nzv.spatialSign#rcv#glmnet 51.794
## All.X#nzv.spatialSign#rcv#glmnet 52.416
## All.X#YeoJohnson#rcv#glmnet 58.542
## All.X.Inc#nzv.spatialSign#rcv#glmnet 67.172
## All.X#expoTrans#rcv#glmnet 70.569
## RFE.X#expoTrans#rcv#glmnet 71.462
## RFE.X#zv.pca#rcv#glmnet 74.676
## All.X#zv.pca#rcv#glmnet 77.239
## RFE.X##rcv#gbm 81.851
## RFE.X##rcv#rf 83.830
## RFE.X#YeoJohnson#rcv#glmnet 84.187
## RFE.X##rcv#nnet 90.316
## RFE.X##rcv#earth 166.648
## RFE.X##rcv#avNNet 182.550
## RFE.X#nzv.pca.spatialSign#rcv#glmnet 533.967
## RFE.X##rcv#bagEarth 884.423
## RFE.X.Inc##rcv#bagEarth 1752.739## label step_major step_minor label_minor bgn end
## 19 fit.models_1_preProc 1 18 preProc 260.190 261.682
## 20 fit.models_1_end 1 19 teardown 261.683 NA
## elapsed
## 19 1.492
## 20 NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1 1 0 0 6.813 261.688 254.875
## 2 fit.models 1 1 1 261.688 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 261.782 NA NArequire(reshape2)## Loading required package: reshape2plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
# print(plt_models_df)
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x= id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 8 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 8 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols])## id max.Accuracy.OOB max.AUCROCR.OOB
## 48 RFE.X.Inc##rcv#bagEarth 0.5838565 0.5840728
## 40 RFE.X##rcv#bagEarth 0.5838565 0.5829523
## 39 RFE.X#zv.pca#rcv#glmnet 0.5829596 0.5816510
## 22 All.X#nzv.spatialSign#rcv#glmnet 0.5820628 0.5787416
## 20 All.X#spatialSign#rcv#glmnet 0.5811659 0.5793099
## 37 RFE.X#nzv.spatialSign#rcv#glmnet 0.5811659 0.5785672
## 23 All.X.Inc#nzv.spatialSign#rcv#glmnet 0.5811659 0.5734523
## 35 RFE.X#spatialSign#rcv#glmnet 0.5802691 0.5794035
## 10 All.X#nzv#rcv#glmnet 0.5775785 0.5766539
## 26 RFE.X#nzv#rcv#glmnet 0.5775785 0.5762019
## 13 All.X#expoTrans#rcv#glmnet 0.5775785 0.5745421
## 29 RFE.X#expoTrans#rcv#glmnet 0.5775785 0.5745421
## 28 RFE.X#YeoJohnson#rcv#glmnet 0.5775785 0.5744824
## 12 All.X#YeoJohnson#rcv#glmnet 0.5775785 0.5744824
## 43 RFE.X##rcv#rpart 0.5775785 0.5547284
## 24 RFE.X##rcv#glmnet 0.5766816 0.5757966
## 25 RFE.X#zv#rcv#glmnet 0.5766816 0.5757966
## 27 RFE.X#BoxCox#rcv#glmnet 0.5766816 0.5757966
## 30 RFE.X#center#rcv#glmnet 0.5766816 0.5757966
## 31 RFE.X#scale#rcv#glmnet 0.5766816 0.5757966
## 32 RFE.X#center.scale#rcv#glmnet 0.5766816 0.5757966
## 33 RFE.X#range#rcv#glmnet 0.5766816 0.5757966
## 36 RFE.X#conditionalX#rcv#glmnet 0.5766816 0.5757966
## 6 Low.cor.X##rcv#glmnet 0.5766816 0.5757966
## 7 All.X##rcv#glmnet 0.5766816 0.5757966
## 9 All.X#zv#rcv#glmnet 0.5766816 0.5757966
## 11 All.X#BoxCox#rcv#glmnet 0.5766816 0.5757966
## 14 All.X#center#rcv#glmnet 0.5766816 0.5757966
## 15 All.X#scale#rcv#glmnet 0.5766816 0.5757966
## 16 All.X#center.scale#rcv#glmnet 0.5766816 0.5757966
## 17 All.X#range#rcv#glmnet 0.5766816 0.5757966
## 21 All.X#conditionalX#rcv#glmnet 0.5766816 0.5757966
## 18 All.X#zv.pca#rcv#glmnet 0.5748879 0.5798104
## 44 RFE.X##rcv#gbm 0.5730942 0.5783993
## 38 RFE.X#nzv.pca.spatialSign#rcv#glmnet 0.5713004 0.5742305
## 47 RFE.X##rcv#earth 0.5677130 0.5720864
## 41 RFE.X##rcv#glm 0.5668161 0.5690413
## 8 All.X##rcv#glm 0.5650224 0.5687249
## 46 RFE.X##rcv#rf 0.5650224 0.5510859
## 45 RFE.X##rcv#avNNet 0.5632287 0.5713598
## 42 RFE.X##rcv#nnet 0.5623318 0.5667713
## 34 RFE.X#ica#rcv#glmnet 0.5363229 0.5370894
## 19 All.X#ica#rcv#glmnet 0.5354260 0.5360626
## 5 Interact.High.cor.Y##rcv#glmnet 0.5345291 0.5242069
## 2 Random###myrandom_classfr 0.5300448 0.5181895
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5300448 0.5102459
## 4 Max.cor.Y##rcv#rpart 0.5300448 0.5000646
## 1 MFO###myMFO_classfr 0.5300448 0.5000000
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 48 0.5671007 0.6169886 0.50
## 40 0.5601161 0.6288811 0.50
## 39 0.5792308 0.6459313 0.50
## 22 0.5572148 0.6478785 0.50
## 20 0.5570372 0.6492270 0.50
## 37 0.5597528 0.6462317 0.50
## 23 0.5556503 0.6440612 0.50
## 35 0.5570372 0.6490772 0.50
## 10 0.5550900 0.6475806 0.50
## 26 0.5531816 0.6466826 0.50
## 13 0.5559554 0.6463076 0.50
## 29 0.5559554 0.6462327 0.50
## 28 0.5559554 0.6466820 0.50
## 12 0.5559554 0.6465321 0.50
## 43 0.5677723 0.6207805 0.50
## 24 0.5588180 0.6475052 0.50
## 25 0.5588180 0.6475052 0.50
## 27 0.5588180 0.6475052 0.50
## 30 0.5588180 0.6475052 0.50
## 31 0.5588180 0.6475052 0.50
## 32 0.5588180 0.6475052 0.50
## 33 0.5588180 0.6475052 0.50
## 36 0.5588180 0.6475052 0.50
## 6 0.5588180 0.6471308 0.50
## 7 0.5588180 0.6471308 0.50
## 9 0.5588180 0.6471308 0.50
## 11 0.5588180 0.6471308 0.50
## 14 0.5588180 0.6471308 0.50
## 15 0.5588180 0.6471308 0.50
## 16 0.5588180 0.6471308 0.50
## 17 0.5588180 0.6471308 0.50
## 21 0.5588180 0.6471308 0.50
## 18 0.5717247 0.6421158 0.50
## 44 0.5638748 0.6477310 0.50
## 38 0.5545298 0.6480297 0.50
## 47 0.5641493 0.6400213 0.50
## 41 0.5497475 0.6255716 0.50
## 8 0.5487933 0.6254219 0.50
## 46 0.5382083 1.0000000 0.50
## 45 0.5593589 0.6367252 0.50
## 42 0.5584628 0.6155403 0.50
## 34 0.5121737 0.5603719 0.50
## 19 0.5133442 0.5607462 0.50
## 5 0.5218093 0.6250526 0.50
## 2 0.4836608 0.5299798 0.55
## 3 0.4999322 0.6240737 0.45
## 4 0.4999322 0.6227308 0.50
## 1 0.5000000 0.5299798 0.50
## opt.prob.threshold.OOB
## 48 0.60
## 40 0.60
## 39 0.50
## 22 0.60
## 20 0.60
## 37 0.60
## 23 0.60
## 35 0.60
## 10 0.60
## 26 0.55
## 13 0.55
## 29 0.55
## 28 0.55
## 12 0.55
## 43 0.50
## 24 0.55
## 25 0.55
## 27 0.55
## 30 0.55
## 31 0.55
## 32 0.55
## 33 0.55
## 36 0.55
## 6 0.55
## 7 0.55
## 9 0.55
## 11 0.55
## 14 0.55
## 15 0.55
## 16 0.55
## 17 0.55
## 21 0.55
## 18 0.50
## 44 0.60
## 38 0.55
## 47 0.60
## 41 0.65
## 8 0.65
## 46 0.65
## 45 0.65
## 42 0.65
## 34 0.55
## 19 0.55
## 5 0.70
## 2 0.55
## 3 0.65
## 4 0.65
## 1 0.50# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(glbgetModelSelectFormula())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7fefd26ef348>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: RFE.X.Inc##rcv#bagEarth"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(glbgetModelSelectFormula(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- glbgetDisplayModelsDf()
}
if (is.null(glbMdlSelId))
glbMdlSelId <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glbMdlSelId))
# knitr crashes sometimes with plot.glmnet
if (!(is.null(knitr::opts_current$get(name = 'label'))) &&
(myparseMdlId(glbMdlSelId)$alg == "glmnet"))
print(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]]) else
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])## [1] "myprint_mdl: B:"
## [1] 50
## [1] "myprint_mdl: oob:"
## Accuracy Kappa
## [1,] 0.6226415 0.2402539
## [2,] 0.6091185 0.2151409
## [3,] 0.6124924 0.2253577
## [4,] 0.6183486 0.2352597
## [5,] 0.6236162 0.2438416
## [6,] 0.6218750 0.2401788
## [7,] 0.6234004 0.2451792
## [8,] 0.6189320 0.2378641
## [9,] 0.5756277 0.1788796
## [10,] 0.6070786 0.2104159
## [11,] 0.6025332 0.2011607
## [12,] 0.6232416 0.2407789
## [13,] 0.6081835 0.2137315
## [14,] 0.6218139 0.2407987
## [15,] 0.6260816 0.2548410
## [16,] 0.6225166 0.2435418
## [17,] 0.6142415 0.2274869
## [18,] 0.6278497 0.2551674
## [19,] 0.6173752 0.2335123
## [20,] 0.6217523 0.2367102
## [21,] 0.6168168 0.2311406
## [22,] 0.6142771 0.2253163
## [23,] 0.6289111 0.2567465
## [24,] 0.6122324 0.2230567
## [25,] 0.6160389 0.2324993
## [26,] 0.6160768 0.2246579
## [27,] 0.5723077 0.1537302
## [28,] 0.6196923 0.2355153
## [29,] 0.6312613 0.2615633
## [30,] 0.6246282 0.2449752
## [31,] 0.6375758 0.2711422
## [32,] 0.6274876 0.2494740
## [33,] 0.6170081 0.2353587
## [34,] 0.6245466 0.2437702
## [35,] 0.6226766 0.2438251
## [36,] 0.6085366 0.2152057
## [37,] 0.6314826 0.2614055
## [38,] 0.6422074 0.2836390
## [39,] 0.6131250 0.2233643
## [40,] 0.6255243 0.2486720
## [41,] 0.6347342 0.2692586
## [42,] 0.6165368 0.2318048
## [43,] 0.6497555 0.2935522
## [44,] 0.6175758 0.2343348
## [45,] 0.6185504 0.2368381
## [46,] 0.6330446 0.2619743
## [47,] 0.6439533 0.2831433
## [48,] 0.6381698 0.2735329
## [49,] 0.6163714 0.2305097
## [50,] 0.6271290 0.2484922
## [1] "myprint_mdl: levels:"
## [1] "D" "R"
## [1] "myprint_mdl: weights:"
## [1] TRUE
## [1] "myprint_mdl: xNames:"
## [1] "Gender.fctrF"
## [2] "Gender.fctrM"
## [3] "Q100010.fctrNo"
## [4] "Q100010.fctrYes"
## [5] "Q100562.fctrNo"
## [6] "Q100562.fctrYes"
## [7] "Q100680.fctrNo"
## [8] "Q100680.fctrYes"
## [9] "Q101162.fctrOptimist"
## [10] "Q101162.fctrPessimist"
## [11] "Q101596.fctrNo"
## [12] "Q101596.fctrYes"
## [13] "Q102089.fctrOwn"
## [14] "Q102089.fctrRent"
## [15] "Q102289.fctrNo"
## [16] "Q102289.fctrYes"
## [17] "Q102687.fctrNo"
## [18] "Q102687.fctrYes"
## [19] "Q102906.fctrNo"
## [20] "Q102906.fctrYes"
## [21] "Q103293.fctrNo"
## [22] "Q103293.fctrYes"
## [23] "Q105655.fctrNo"
## [24] "Q105655.fctrYes"
## [25] "Q105840.fctrNo"
## [26] "Q105840.fctrYes"
## [27] "Q106042.fctrNo"
## [28] "Q106042.fctrYes"
## [29] "Q106272.fctrNo"
## [30] "Q106272.fctrYes"
## [31] "Q106388.fctrNo"
## [32] "Q106388.fctrYes"
## [33] "Q106389.fctrNo"
## [34] "Q106389.fctrYes"
## [35] "Q106993.fctrNo"
## [36] "Q106993.fctrYes"
## [37] "Q107491.fctrNo"
## [38] "Q107491.fctrYes"
## [39] "Q107869.fctrNo"
## [40] "Q107869.fctrYes"
## [41] "Q108617.fctrNo"
## [42] "Q108617.fctrYes"
## [43] "Q108754.fctrNo"
## [44] "Q108754.fctrYes"
## [45] "Q108855.fctrUmm..."
## [46] "Q108855.fctrYes!"
## [47] "Q108856.fctrSocialize"
## [48] "Q108856.fctrSpace"
## [49] "Q108950.fctrCautious"
## [50] "Q108950.fctrRisk-friendly"
## [51] "Q109244.fctrNo"
## [52] "Q109244.fctrYes"
## [53] "Edn.fctr.L"
## [54] "Edn.fctr.Q"
## [55] "Edn.fctr.C"
## [56] "Edn.fctr^4"
## [57] "Edn.fctr^5"
## [58] "Edn.fctr^6"
## [59] "Edn.fctr^7"
## [60] "Hhold.fctrMKn"
## [61] "Hhold.fctrMKy"
## [62] "Hhold.fctrPKn"
## [63] "Hhold.fctrPKy"
## [64] "Hhold.fctrSKn"
## [65] "Hhold.fctrSKy"
## [66] "Income.fctr.L"
## [67] "Income.fctr.Q"
## [68] "Income.fctr.C"
## [69] "Income.fctr^4"
## [70] "Income.fctr^5"
## [71] "Income.fctr^6"
## [72] "Q100689.fctrNo"
## [73] "Q100689.fctrYes"
## [74] "Q101163.fctrDad"
## [75] "Q101163.fctrMom"
## [76] "Q102674.fctrNo"
## [77] "Q102674.fctrYes"
## [78] "Q104996.fctrNo"
## [79] "Q104996.fctrYes"
## [80] "Q106997.fctrGr"
## [81] "Q106997.fctrYy"
## [82] "Q108342.fctrIn-person"
## [83] "Q108342.fctrOnline"
## [84] "Q108343.fctrNo"
## [85] "Q108343.fctrYes"
## [86] "Q113181.fctrNo"
## [87] "Q113181.fctrYes"
## [88] "Q115611.fctrNo"
## [89] "Q115611.fctrYes"
## [90] "Q116881.fctrHappy"
## [91] "Q116881.fctrRight"
## [92] "Q119334.fctrNo"
## [93] "Q119334.fctrYes"
## [94] "Q119650.fctrGiving"
## [95] "Q119650.fctrReceiving"
## [96] "Q120379.fctrNo"
## [97] "Q120379.fctrYes"
## [98] "Q121699.fctrNo"
## [99] "Q121699.fctrYes"
## [100] "Q122771.fctrPc"
## [101] "Q122771.fctrPt"
## [102] "Q123621.fctrNo"
## [103] "Q123621.fctrYes"
## [104] "Q124122.fctrNo"
## [105] "Q124122.fctrYes"
## [106] "Q98197.fctrNo"
## [107] "Q98197.fctrYes"
## [108] "YOB.Age.fctr.L"
## [109] "YOB.Age.fctr.Q"
## [110] "YOB.Age.fctr.C"
## [111] "YOB.Age.fctr^4"
## [112] "YOB.Age.fctr^5"
## [113] "YOB.Age.fctr^6"
## [114] "YOB.Age.fctr^7"
## [115] "YOB.Age.fctr^8"
## [116] "Q109367.fctrNo"
## [117] "Q109367.fctrYes"
## [118] "Q110740.fctrMac"
## [119] "Q110740.fctrPC"
## [120] "Q111220.fctrNo"
## [121] "Q111220.fctrYes"
## [122] "Q111580.fctrDemanding"
## [123] "Q111580.fctrSupportive"
## [124] "Q111848.fctrNo"
## [125] "Q111848.fctrYes"
## [126] "Q112270.fctrNo"
## [127] "Q112270.fctrYes"
## [128] "Q112478.fctrNo"
## [129] "Q112478.fctrYes"
## [130] "Q112512.fctrNo"
## [131] "Q112512.fctrYes"
## [132] "Q113583.fctrTalk"
## [133] "Q113583.fctrTunes"
## [134] "Q113584.fctrPeople"
## [135] "Q113584.fctrTechnology"
## [136] "Q113992.fctrNo"
## [137] "Q113992.fctrYes"
## [138] "Q114152.fctrNo"
## [139] "Q114152.fctrYes"
## [140] "Q114386.fctrMysterious"
## [141] "Q114386.fctrTMI"
## [142] "Q114517.fctrNo"
## [143] "Q114517.fctrYes"
## [144] "Q114748.fctrNo"
## [145] "Q114748.fctrYes"
## [146] "Q114961.fctrNo"
## [147] "Q114961.fctrYes"
## [148] "Q115195.fctrNo"
## [149] "Q115195.fctrYes"
## [150] "Q115390.fctrNo"
## [151] "Q115390.fctrYes"
## [152] "Q115602.fctrNo"
## [153] "Q115602.fctrYes"
## [154] "Q115610.fctrNo"
## [155] "Q115610.fctrYes"
## [156] "Q115777.fctrEnd"
## [157] "Q115777.fctrStart"
## [158] "Q115899.fctrCs"
## [159] "Q115899.fctrMe"
## [160] "Q116197.fctrA.M."
## [161] "Q116197.fctrP.M."
## [162] "Q116441.fctrNo"
## [163] "Q116441.fctrYes"
## [164] "Q116448.fctrNo"
## [165] "Q116448.fctrYes"
## [166] "Q116601.fctrNo"
## [167] "Q116601.fctrYes"
## [168] "Q116797.fctrNo"
## [169] "Q116797.fctrYes"
## [170] "Q116953.fctrNo"
## [171] "Q116953.fctrYes"
## [172] "Q117186.fctrCool headed"
## [173] "Q117186.fctrHot headed"
## [174] "Q117193.fctrOdd hours"
## [175] "Q117193.fctrStandard hours"
## [176] "Q118117.fctrNo"
## [177] "Q118117.fctrYes"
## [178] "Q118232.fctrId"
## [179] "Q118232.fctrPr"
## [180] "Q118233.fctrNo"
## [181] "Q118233.fctrYes"
## [182] "Q118237.fctrNo"
## [183] "Q118237.fctrYes"
## [184] "Q118892.fctrNo"
## [185] "Q118892.fctrYes"
## [186] "Q119851.fctrNo"
## [187] "Q119851.fctrYes"
## [188] "Q120012.fctrNo"
## [189] "Q120012.fctrYes"
## [190] "Q120014.fctrNo"
## [191] "Q120014.fctrYes"
## [192] "Q120194.fctrStudy first"
## [193] "Q120194.fctrTry first"
## [194] "Q120472.fctrArt"
## [195] "Q120472.fctrScience"
## [196] "Q120650.fctrNo"
## [197] "Q120650.fctrYes"
## [198] "Q120978.fctrNo"
## [199] "Q120978.fctrYes"
## [200] "Q121011.fctrNo"
## [201] "Q121011.fctrYes"
## [202] "Q121700.fctrNo"
## [203] "Q121700.fctrYes"
## [204] "Q122120.fctrNo"
## [205] "Q122120.fctrYes"
## [206] "Q122769.fctrNo"
## [207] "Q122769.fctrYes"
## [208] "Q122770.fctrNo"
## [209] "Q122770.fctrYes"
## [210] "Q123464.fctrNo"
## [211] "Q123464.fctrYes"
## [212] "Q124742.fctrNo"
## [213] "Q124742.fctrYes"
## [214] "Q96024.fctrNo"
## [215] "Q96024.fctrYes"
## [216] "Q98078.fctrNo"
## [217] "Q98078.fctrYes"
## [218] "Q98578.fctrNo"
## [219] "Q98578.fctrYes"
## [220] "Q98869.fctrNo"
## [221] "Q98869.fctrYes"
## [222] "Q99480.fctrNo"
## [223] "Q99480.fctrYes"
## [224] "Q99581.fctrNo"
## [225] "Q99581.fctrYes"
## [226] "Q99716.fctrNo"
## [227] "Q99716.fctrYes"
## [228] "Q99982.fctrCheck!"
## [229] "Q99982.fctrNope"
## [230] "Q109244.fctrNo:Edn.fctr.L"
## [231] "Q109244.fctrYes:Edn.fctr.L"
## [232] "Q109244.fctrNo:Edn.fctr.Q"
## [233] "Q109244.fctrYes:Edn.fctr.Q"
## [234] "Q109244.fctrNo:Edn.fctr.C"
## [235] "Q109244.fctrYes:Edn.fctr.C"
## [236] "Q109244.fctrNo:Edn.fctr^4"
## [237] "Q109244.fctrYes:Edn.fctr^4"
## [238] "Q109244.fctrNo:Edn.fctr^5"
## [239] "Q109244.fctrYes:Edn.fctr^5"
## [240] "Q109244.fctrNo:Edn.fctr^6"
## [241] "Q109244.fctrYes:Edn.fctr^6"
## [242] "Q109244.fctrNo:Edn.fctr^7"
## [243] "Q109244.fctrYes:Edn.fctr^7"
## [244] "Q109244.fctrNo:Hhold.fctrMKn"
## [245] "Q109244.fctrYes:Hhold.fctrMKn"
## [246] "Q109244.fctrNo:Hhold.fctrMKy"
## [247] "Q109244.fctrYes:Hhold.fctrMKy"
## [248] "Q109244.fctrNo:Hhold.fctrPKn"
## [249] "Q109244.fctrYes:Hhold.fctrPKn"
## [250] "Q109244.fctrNo:Hhold.fctrPKy"
## [251] "Q109244.fctrYes:Hhold.fctrPKy"
## [252] "Q109244.fctrNo:Hhold.fctrSKn"
## [253] "Q109244.fctrYes:Hhold.fctrSKn"
## [254] "Q109244.fctrNo:Hhold.fctrSKy"
## [255] "Q109244.fctrYes:Hhold.fctrSKy"
## [256] "Q109244.fctrNo:Income.fctr.L"
## [257] "Q109244.fctrYes:Income.fctr.L"
## [258] "Q109244.fctrNo:Income.fctr.Q"
## [259] "Q109244.fctrYes:Income.fctr.Q"
## [260] "Q109244.fctrNo:Income.fctr.C"
## [261] "Q109244.fctrYes:Income.fctr.C"
## [262] "Q109244.fctrNo:Income.fctr^4"
## [263] "Q109244.fctrYes:Income.fctr^4"
## [264] "Q109244.fctrNo:Income.fctr^5"
## [265] "Q109244.fctrYes:Income.fctr^5"
## [266] "Q109244.fctrNo:Income.fctr^6"
## [267] "Q109244.fctrYes:Income.fctr^6"
## [268] "Q109244.fctrNo:Q100689.fctrNo"
## [269] "Q109244.fctrYes:Q100689.fctrNo"
## [270] "Q109244.fctrNo:Q100689.fctrYes"
## [271] "Q109244.fctrYes:Q100689.fctrYes"
## [272] "Q109244.fctrNo:Q101163.fctrDad"
## [273] "Q109244.fctrYes:Q101163.fctrDad"
## [274] "Q109244.fctrNo:Q101163.fctrMom"
## [275] "Q109244.fctrYes:Q101163.fctrMom"
## [276] "Q109244.fctrNo:Q102674.fctrNo"
## [277] "Q109244.fctrYes:Q102674.fctrNo"
## [278] "Q109244.fctrNo:Q102674.fctrYes"
## [279] "Q109244.fctrYes:Q102674.fctrYes"
## [280] "Q109244.fctrNo:Q104996.fctrNo"
## [281] "Q109244.fctrYes:Q104996.fctrNo"
## [282] "Q109244.fctrNo:Q104996.fctrYes"
## [283] "Q109244.fctrYes:Q104996.fctrYes"
## [284] "Q109244.fctrNo:Q106997.fctrGr"
## [285] "Q109244.fctrYes:Q106997.fctrGr"
## [286] "Q109244.fctrNo:Q106997.fctrYy"
## [287] "Q109244.fctrYes:Q106997.fctrYy"
## [288] "Q109244.fctrNo:Q108342.fctrIn-person"
## [289] "Q109244.fctrYes:Q108342.fctrIn-person"
## [290] "Q109244.fctrNo:Q108342.fctrOnline"
## [291] "Q109244.fctrYes:Q108342.fctrOnline"
## [292] "Q109244.fctrNo:Q108343.fctrNo"
## [293] "Q109244.fctrYes:Q108343.fctrNo"
## [294] "Q109244.fctrNo:Q108343.fctrYes"
## [295] "Q109244.fctrYes:Q108343.fctrYes"
## [296] "Q109244.fctrNo:Q113181.fctrNo"
## [297] "Q109244.fctrYes:Q113181.fctrNo"
## [298] "Q109244.fctrNo:Q113181.fctrYes"
## [299] "Q109244.fctrYes:Q113181.fctrYes"
## [300] "Q109244.fctrNo:Q115611.fctrNo"
## [301] "Q109244.fctrYes:Q115611.fctrNo"
## [302] "Q109244.fctrNo:Q115611.fctrYes"
## [303] "Q109244.fctrYes:Q115611.fctrYes"
## [304] "Q109244.fctrNo:Q116881.fctrHappy"
## [305] "Q109244.fctrYes:Q116881.fctrHappy"
## [306] "Q109244.fctrNo:Q116881.fctrRight"
## [307] "Q109244.fctrYes:Q116881.fctrRight"
## [308] "Q109244.fctrNo:Q119334.fctrNo"
## [309] "Q109244.fctrYes:Q119334.fctrNo"
## [310] "Q109244.fctrNo:Q119334.fctrYes"
## [311] "Q109244.fctrYes:Q119334.fctrYes"
## [312] "Q109244.fctrNo:Q119650.fctrGiving"
## [313] "Q109244.fctrYes:Q119650.fctrGiving"
## [314] "Q109244.fctrNo:Q119650.fctrReceiving"
## [315] "Q109244.fctrYes:Q119650.fctrReceiving"
## [316] "Q109244.fctrNo:Q120379.fctrNo"
## [317] "Q109244.fctrYes:Q120379.fctrNo"
## [318] "Q109244.fctrNo:Q120379.fctrYes"
## [319] "Q109244.fctrYes:Q120379.fctrYes"
## [320] "Q109244.fctrNo:Q121699.fctrNo"
## [321] "Q109244.fctrYes:Q121699.fctrNo"
## [322] "Q109244.fctrNo:Q121699.fctrYes"
## [323] "Q109244.fctrYes:Q121699.fctrYes"
## [324] "Q109244.fctrNo:Q122771.fctrPc"
## [325] "Q109244.fctrYes:Q122771.fctrPc"
## [326] "Q109244.fctrNo:Q122771.fctrPt"
## [327] "Q109244.fctrYes:Q122771.fctrPt"
## [328] "Q109244.fctrNo:Q123621.fctrNo"
## [329] "Q109244.fctrYes:Q123621.fctrNo"
## [330] "Q109244.fctrNo:Q123621.fctrYes"
## [331] "Q109244.fctrYes:Q123621.fctrYes"
## [332] "Q109244.fctrNo:Q124122.fctrNo"
## [333] "Q109244.fctrYes:Q124122.fctrNo"
## [334] "Q109244.fctrNo:Q124122.fctrYes"
## [335] "Q109244.fctrYes:Q124122.fctrYes"
## [336] "Q109244.fctrNo:Q98197.fctrNo"
## [337] "Q109244.fctrYes:Q98197.fctrNo"
## [338] "Q109244.fctrNo:Q98197.fctrYes"
## [339] "Q109244.fctrYes:Q98197.fctrYes"
## [340] "Q109244.fctrNo:YOB.Age.fctr.L"
## [341] "Q109244.fctrYes:YOB.Age.fctr.L"
## [342] "Q109244.fctrNo:YOB.Age.fctr.Q"
## [343] "Q109244.fctrYes:YOB.Age.fctr.Q"
## [344] "Q109244.fctrNo:YOB.Age.fctr.C"
## [345] "Q109244.fctrYes:YOB.Age.fctr.C"
## [346] "Q109244.fctrNo:YOB.Age.fctr^4"
## [347] "Q109244.fctrYes:YOB.Age.fctr^4"
## [348] "Q109244.fctrNo:YOB.Age.fctr^5"
## [349] "Q109244.fctrYes:YOB.Age.fctr^5"
## [350] "Q109244.fctrNo:YOB.Age.fctr^6"
## [351] "Q109244.fctrYes:YOB.Age.fctr^6"
## [352] "Q109244.fctrNo:YOB.Age.fctr^7"
## [353] "Q109244.fctrYes:YOB.Age.fctr^7"
## [354] "Q109244.fctrNo:YOB.Age.fctr^8"
## [355] "Q109244.fctrYes:YOB.Age.fctr^8"
## [356] "Q109244.fctrNA:.clusterid.fctr2"
## [357] "Q109244.fctrNo:.clusterid.fctr2"
## [358] "Q109244.fctrYes:.clusterid.fctr2"
## [359] "Q109244.fctrNA:.clusterid.fctr3"
## [360] "Q109244.fctrNo:.clusterid.fctr3"
## [361] "Q109244.fctrYes:.clusterid.fctr3"
## [362] "YOB.Age.fctrNA:YOB.Age.dff"
## [363] "YOB.Age.fctr(15,20]:YOB.Age.dff"
## [364] "YOB.Age.fctr(20,25]:YOB.Age.dff"
## [365] "YOB.Age.fctr(25,30]:YOB.Age.dff"
## [366] "YOB.Age.fctr(30,35]:YOB.Age.dff"
## [367] "YOB.Age.fctr(35,40]:YOB.Age.dff"
## [368] "YOB.Age.fctr(40,50]:YOB.Age.dff"
## [369] "YOB.Age.fctr(50,65]:YOB.Age.dff"
## [370] "YOB.Age.fctr(65,90]:YOB.Age.dff"
## [1] "myprint_mdl: problemType:"
## [1] "Classification"
## [1] "myprint_mdl: tuneValue:"
## nprune degree
## 1 256 1
## [1] "myprint_mdl: obsLevels:"
## [1] "D" "R"## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))## [1] "RFE.X.Inc##rcv#bagEarth fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))## [1] "RFE.X.Inc##rcv#bagEarth OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## RFE.X.Inc..rcv.bagEarth.imp
## Q109244.fctrYes 100.00000000
## Q109244.fctrNo:Q113181.fctrYes 64.54013369
## Q115611.fctrYes 55.61206173
## Q98197.fctrNo 52.69690976
## Q109244.fctrNo:Q119650.fctrGiving 48.42576135
## Q116881.fctrHappy 46.09192006
## Q109244.fctrNo:Income.fctr.L 43.29279743
## Hhold.fctrPKn 41.17887794
## Q106997.fctrGr 39.52523209
## Q108342.fctrOnline 37.78484599
## Edn.fctr^7 36.29940771
## Q109244.fctrYes:Income.fctr.L 34.85072480
## Q120379.fctrNo 33.74249405
## Q109244.fctrNo:Income.fctr.C 32.06273684
## Q109244.fctrNo:Edn.fctr.L 30.88881371
## Q109244.fctrYes:Q116881.fctrHappy 29.38897891
## Q122771.fctrPt 28.00320961
## Q100689.fctrYes 26.86898299
## Q123621.fctrYes 25.62710047
## Q109244.fctrYes:Q123621.fctrYes 25.21567205
## Q121011.fctrNo 23.75398503
## Q101163.fctrDad 22.29207105
## Q109244.fctrYes:Q101163.fctrMom 21.27931275
## Q109244.fctrNo:Q119334.fctrYes 20.02571954
## Q124122.fctrYes 18.64516818
## Q121699.fctrYes 17.89699784
## Q122771.fctrPc 17.06512114
## Q104996.fctrYes 15.36384707
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.57949576
## Q108343.fctrYes 13.21156227
## Q102674.fctrYes 12.08112176
## YOB.Age.fctr(40,50]:YOB.Age.dff 10.93839173
## YOB.Age.fctr^7 9.40830535
## Q109244.fctrYes:Edn.fctr^7 8.86574323
## Q115611.fctrNo 7.37062918
## YOB.Age.fctr.L 6.57320444
## Q109244.fctrNo:YOB.Age.fctr^6 5.90465515
## Q109244.fctrNo:YOB.Age.fctr^7 4.91402305
## YOB.Age.fctr(25,30]:YOB.Age.dff 3.89746513
## Q109244.fctrYes:Q115611.fctrNo 3.25707597
## Q109244.fctrYes:.clusterid.fctr3 2.56126079
## Q109244.fctrYes:Edn.fctr.L 1.78716549
## Gender.fctrF 1.41987740
## Gender.fctrM 1.09707638
## Q100010.fctrYes 0.72555596
## Q100010.fctrNo 0.56730464
## Q100562.fctrNo 0.33475266
## Q100562.fctrYes 0.04896745
## Q100680.fctrNo 0.00000000
## Q100680.fctrYes 0.00000000
## Q101162.fctrOptimist 0.00000000
## Q101162.fctrPessimist 0.00000000
## Q101596.fctrNo 0.00000000
## Q101596.fctrYes 0.00000000
## Q102089.fctrOwn 0.00000000
## Q102089.fctrRent 0.00000000
## Q102289.fctrNo 0.00000000
## Q102289.fctrYes 0.00000000
## Q102687.fctrNo 0.00000000
## Q102687.fctrYes 0.00000000
## Q102906.fctrNo 0.00000000
## Q102906.fctrYes 0.00000000
## Q103293.fctrNo 0.00000000
## Q103293.fctrYes 0.00000000
## Q105655.fctrNo 0.00000000
## Q105655.fctrYes 0.00000000
## Q105840.fctrNo 0.00000000
## Q105840.fctrYes 0.00000000
## Q106042.fctrNo 0.00000000
## Q106042.fctrYes 0.00000000
## Q106272.fctrNo 0.00000000
## Q106272.fctrYes 0.00000000
## Q106388.fctrNo 0.00000000
## Q106388.fctrYes 0.00000000
## Q106389.fctrNo 0.00000000
## Q106389.fctrYes 0.00000000
## Q106993.fctrNo 0.00000000
## Q106993.fctrYes 0.00000000
## Q107491.fctrNo 0.00000000
## Q107491.fctrYes 0.00000000
## Q107869.fctrNo 0.00000000
## Q107869.fctrYes 0.00000000
## Q108617.fctrNo 0.00000000
## Q108617.fctrYes 0.00000000
## Q108754.fctrNo 0.00000000
## Q108754.fctrYes 0.00000000
## Q108855.fctrUmm... 0.00000000
## Q108855.fctrYes! 0.00000000
## Q108856.fctrSocialize 0.00000000
## Q108856.fctrSpace 0.00000000
## Q108950.fctrCautious 0.00000000
## Q108950.fctrRisk-friendly 0.00000000
## Q109244.fctrNo 0.00000000
## Edn.fctr.L 0.00000000
## Edn.fctr.Q 0.00000000
## Edn.fctr.C 0.00000000
## Edn.fctr^4 0.00000000
## Edn.fctr^5 0.00000000
## Edn.fctr^6 0.00000000
## Hhold.fctrMKn 0.00000000
## Hhold.fctrMKy 0.00000000
## Hhold.fctrPKy 0.00000000
## Hhold.fctrSKn 0.00000000
## Hhold.fctrSKy 0.00000000
## Income.fctr.L 0.00000000
## Income.fctr.Q 0.00000000
## Income.fctr.C 0.00000000
## Income.fctr^4 0.00000000
## Income.fctr^5 0.00000000
## Income.fctr^6 0.00000000
## Q100689.fctrNo 0.00000000
## Q101163.fctrMom 0.00000000
## Q102674.fctrNo 0.00000000
## Q104996.fctrNo 0.00000000
## Q106997.fctrYy 0.00000000
## Q108342.fctrIn-person 0.00000000
## Q108343.fctrNo 0.00000000
## Q113181.fctrNo 0.00000000
## Q113181.fctrYes 0.00000000
## Q116881.fctrRight 0.00000000
## Q119334.fctrNo 0.00000000
## Q119334.fctrYes 0.00000000
## Q119650.fctrGiving 0.00000000
## Q119650.fctrReceiving 0.00000000
## Q120379.fctrYes 0.00000000
## Q121699.fctrNo 0.00000000
## Q123621.fctrNo 0.00000000
## Q124122.fctrNo 0.00000000
## Q98197.fctrYes 0.00000000
## YOB.Age.fctr.Q 0.00000000
## YOB.Age.fctr.C 0.00000000
## YOB.Age.fctr^4 0.00000000
## YOB.Age.fctr^5 0.00000000
## YOB.Age.fctr^6 0.00000000
## YOB.Age.fctr^8 0.00000000
## Q109367.fctrNo 0.00000000
## Q109367.fctrYes 0.00000000
## Q110740.fctrMac 0.00000000
## Q110740.fctrPC 0.00000000
## Q111220.fctrNo 0.00000000
## Q111220.fctrYes 0.00000000
## Q111580.fctrDemanding 0.00000000
## Q111580.fctrSupportive 0.00000000
## Q111848.fctrNo 0.00000000
## Q111848.fctrYes 0.00000000
## Q112270.fctrNo 0.00000000
## Q112270.fctrYes 0.00000000
## Q112478.fctrNo 0.00000000
## Q112478.fctrYes 0.00000000
## Q112512.fctrNo 0.00000000
## Q112512.fctrYes 0.00000000
## Q113583.fctrTalk 0.00000000
## Q113583.fctrTunes 0.00000000
## Q113584.fctrPeople 0.00000000
## Q113584.fctrTechnology 0.00000000
## Q113992.fctrNo 0.00000000
## Q113992.fctrYes 0.00000000
## Q114152.fctrNo 0.00000000
## Q114152.fctrYes 0.00000000
## Q114386.fctrMysterious 0.00000000
## Q114386.fctrTMI 0.00000000
## Q114517.fctrNo 0.00000000
## Q114517.fctrYes 0.00000000
## Q114748.fctrNo 0.00000000
## Q114748.fctrYes 0.00000000
## Q114961.fctrNo 0.00000000
## Q114961.fctrYes 0.00000000
## Q115195.fctrNo 0.00000000
## Q115195.fctrYes 0.00000000
## Q115390.fctrNo 0.00000000
## Q115390.fctrYes 0.00000000
## Q115602.fctrNo 0.00000000
## Q115602.fctrYes 0.00000000
## Q115610.fctrNo 0.00000000
## Q115610.fctrYes 0.00000000
## Q115777.fctrEnd 0.00000000
## Q115777.fctrStart 0.00000000
## Q115899.fctrCs 0.00000000
## Q115899.fctrMe 0.00000000
## Q116197.fctrA.M. 0.00000000
## Q116197.fctrP.M. 0.00000000
## Q116441.fctrNo 0.00000000
## Q116441.fctrYes 0.00000000
## Q116448.fctrNo 0.00000000
## Q116448.fctrYes 0.00000000
## Q116601.fctrNo 0.00000000
## Q116601.fctrYes 0.00000000
## Q116797.fctrNo 0.00000000
## Q116797.fctrYes 0.00000000
## Q116953.fctrNo 0.00000000
## Q116953.fctrYes 0.00000000
## Q117186.fctrCoolheaded 0.00000000
## Q117186.fctrHotheaded 0.00000000
## Q117193.fctrOddhours 0.00000000
## Q117193.fctrStandardhours 0.00000000
## Q118117.fctrNo 0.00000000
## Q118117.fctrYes 0.00000000
## Q118232.fctrId 0.00000000
## Q118232.fctrPr 0.00000000
## Q118233.fctrNo 0.00000000
## Q118233.fctrYes 0.00000000
## Q118237.fctrNo 0.00000000
## Q118237.fctrYes 0.00000000
## Q118892.fctrNo 0.00000000
## Q118892.fctrYes 0.00000000
## Q119851.fctrNo 0.00000000
## Q119851.fctrYes 0.00000000
## Q120012.fctrNo 0.00000000
## Q120012.fctrYes 0.00000000
## Q120014.fctrNo 0.00000000
## Q120014.fctrYes 0.00000000
## Q120194.fctrStudyfirst 0.00000000
## Q120194.fctrTryfirst 0.00000000
## Q120472.fctrArt 0.00000000
## Q120472.fctrScience 0.00000000
## Q120650.fctrNo 0.00000000
## Q120650.fctrYes 0.00000000
## Q120978.fctrNo 0.00000000
## Q120978.fctrYes 0.00000000
## Q121011.fctrYes 0.00000000
## Q121700.fctrNo 0.00000000
## Q121700.fctrYes 0.00000000
## Q122120.fctrNo 0.00000000
## Q122120.fctrYes 0.00000000
## Q122769.fctrNo 0.00000000
## Q122769.fctrYes 0.00000000
## Q122770.fctrNo 0.00000000
## Q122770.fctrYes 0.00000000
## Q123464.fctrNo 0.00000000
## Q123464.fctrYes 0.00000000
## Q124742.fctrNo 0.00000000
## Q124742.fctrYes 0.00000000
## Q96024.fctrNo 0.00000000
## Q96024.fctrYes 0.00000000
## Q98078.fctrNo 0.00000000
## Q98078.fctrYes 0.00000000
## Q98578.fctrNo 0.00000000
## Q98578.fctrYes 0.00000000
## Q98869.fctrNo 0.00000000
## Q98869.fctrYes 0.00000000
## Q99480.fctrNo 0.00000000
## Q99480.fctrYes 0.00000000
## Q99581.fctrNo 0.00000000
## Q99581.fctrYes 0.00000000
## Q99716.fctrNo 0.00000000
## Q99716.fctrYes 0.00000000
## Q99982.fctrCheck! 0.00000000
## Q99982.fctrNope 0.00000000
## Q109244.fctrNo:Edn.fctr.Q 0.00000000
## Q109244.fctrYes:Edn.fctr.Q 0.00000000
## Q109244.fctrNo:Edn.fctr.C 0.00000000
## Q109244.fctrYes:Edn.fctr.C 0.00000000
## Q109244.fctrNo:Edn.fctr^4 0.00000000
## Q109244.fctrYes:Edn.fctr^4 0.00000000
## Q109244.fctrNo:Edn.fctr^5 0.00000000
## Q109244.fctrYes:Edn.fctr^5 0.00000000
## Q109244.fctrNo:Edn.fctr^6 0.00000000
## Q109244.fctrYes:Edn.fctr^6 0.00000000
## Q109244.fctrNo:Edn.fctr^7 0.00000000
## Q109244.fctrNo:Hhold.fctrMKn 0.00000000
## Q109244.fctrYes:Hhold.fctrMKn 0.00000000
## Q109244.fctrNo:Hhold.fctrMKy 0.00000000
## Q109244.fctrYes:Hhold.fctrMKy 0.00000000
## Q109244.fctrNo:Hhold.fctrPKn 0.00000000
## Q109244.fctrYes:Hhold.fctrPKn 0.00000000
## Q109244.fctrNo:Hhold.fctrPKy 0.00000000
## Q109244.fctrYes:Hhold.fctrPKy 0.00000000
## Q109244.fctrNo:Hhold.fctrSKn 0.00000000
## Q109244.fctrYes:Hhold.fctrSKn 0.00000000
## Q109244.fctrNo:Hhold.fctrSKy 0.00000000
## Q109244.fctrYes:Hhold.fctrSKy 0.00000000
## Q109244.fctrNo:Income.fctr.Q 0.00000000
## Q109244.fctrYes:Income.fctr.Q 0.00000000
## Q109244.fctrYes:Income.fctr.C 0.00000000
## Q109244.fctrNo:Income.fctr^4 0.00000000
## Q109244.fctrYes:Income.fctr^4 0.00000000
## Q109244.fctrNo:Income.fctr^5 0.00000000
## Q109244.fctrYes:Income.fctr^5 0.00000000
## Q109244.fctrNo:Income.fctr^6 0.00000000
## Q109244.fctrYes:Income.fctr^6 0.00000000
## Q109244.fctrNo:Q100689.fctrNo 0.00000000
## Q109244.fctrYes:Q100689.fctrNo 0.00000000
## Q109244.fctrNo:Q100689.fctrYes 0.00000000
## Q109244.fctrYes:Q100689.fctrYes 0.00000000
## Q109244.fctrNo:Q101163.fctrDad 0.00000000
## Q109244.fctrYes:Q101163.fctrDad 0.00000000
## Q109244.fctrNo:Q101163.fctrMom 0.00000000
## Q109244.fctrNo:Q102674.fctrNo 0.00000000
## Q109244.fctrYes:Q102674.fctrNo 0.00000000
## Q109244.fctrNo:Q102674.fctrYes 0.00000000
## Q109244.fctrYes:Q102674.fctrYes 0.00000000
## Q109244.fctrNo:Q104996.fctrNo 0.00000000
## Q109244.fctrYes:Q104996.fctrNo 0.00000000
## Q109244.fctrNo:Q104996.fctrYes 0.00000000
## Q109244.fctrYes:Q104996.fctrYes 0.00000000
## Q109244.fctrNo:Q106997.fctrGr 0.00000000
## Q109244.fctrYes:Q106997.fctrGr 0.00000000
## Q109244.fctrNo:Q106997.fctrYy 0.00000000
## Q109244.fctrYes:Q106997.fctrYy 0.00000000
## Q109244.fctrNo:Q108342.fctrIn-person 0.00000000
## Q109244.fctrYes:Q108342.fctrIn-person 0.00000000
## Q109244.fctrNo:Q108342.fctrOnline 0.00000000
## Q109244.fctrYes:Q108342.fctrOnline 0.00000000
## Q109244.fctrNo:Q108343.fctrNo 0.00000000
## Q109244.fctrYes:Q108343.fctrNo 0.00000000
## Q109244.fctrNo:Q108343.fctrYes 0.00000000
## Q109244.fctrYes:Q108343.fctrYes 0.00000000
## Q109244.fctrNo:Q113181.fctrNo 0.00000000
## Q109244.fctrYes:Q113181.fctrNo 0.00000000
## Q109244.fctrYes:Q113181.fctrYes 0.00000000
## Q109244.fctrNo:Q115611.fctrNo 0.00000000
## Q109244.fctrNo:Q115611.fctrYes 0.00000000
## Q109244.fctrYes:Q115611.fctrYes 0.00000000
## Q109244.fctrNo:Q116881.fctrHappy 0.00000000
## Q109244.fctrNo:Q116881.fctrRight 0.00000000
## Q109244.fctrYes:Q116881.fctrRight 0.00000000
## Q109244.fctrNo:Q119334.fctrNo 0.00000000
## Q109244.fctrYes:Q119334.fctrNo 0.00000000
## Q109244.fctrYes:Q119334.fctrYes 0.00000000
## Q109244.fctrYes:Q119650.fctrGiving 0.00000000
## Q109244.fctrNo:Q119650.fctrReceiving 0.00000000
## Q109244.fctrYes:Q119650.fctrReceiving 0.00000000
## Q109244.fctrNo:Q120379.fctrNo 0.00000000
## Q109244.fctrYes:Q120379.fctrNo 0.00000000
## Q109244.fctrNo:Q120379.fctrYes 0.00000000
## Q109244.fctrYes:Q120379.fctrYes 0.00000000
## Q109244.fctrNo:Q121699.fctrNo 0.00000000
## Q109244.fctrYes:Q121699.fctrNo 0.00000000
## Q109244.fctrNo:Q121699.fctrYes 0.00000000
## Q109244.fctrYes:Q121699.fctrYes 0.00000000
## Q109244.fctrNo:Q122771.fctrPc 0.00000000
## Q109244.fctrYes:Q122771.fctrPc 0.00000000
## Q109244.fctrNo:Q122771.fctrPt 0.00000000
## Q109244.fctrYes:Q122771.fctrPt 0.00000000
## Q109244.fctrNo:Q123621.fctrNo 0.00000000
## Q109244.fctrYes:Q123621.fctrNo 0.00000000
## Q109244.fctrNo:Q123621.fctrYes 0.00000000
## Q109244.fctrNo:Q124122.fctrNo 0.00000000
## Q109244.fctrYes:Q124122.fctrNo 0.00000000
## Q109244.fctrNo:Q124122.fctrYes 0.00000000
## Q109244.fctrYes:Q124122.fctrYes 0.00000000
## Q109244.fctrNo:Q98197.fctrNo 0.00000000
## Q109244.fctrYes:Q98197.fctrNo 0.00000000
## Q109244.fctrNo:Q98197.fctrYes 0.00000000
## Q109244.fctrYes:Q98197.fctrYes 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.L 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.L 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.Q 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.Q 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.C 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.C 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^4 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^4 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^5 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^5 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^6 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^7 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^8 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^8 0.00000000
## Q109244.fctrNA:.clusterid.fctr2 0.00000000
## Q109244.fctrNo:.clusterid.fctr2 0.00000000
## Q109244.fctrYes:.clusterid.fctr2 0.00000000
## Q109244.fctrNA:.clusterid.fctr3 0.00000000
## Q109244.fctrNo:.clusterid.fctr3 0.00000000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000000
## imp
## Q109244.fctrYes 100.00000000
## Q109244.fctrNo:Q113181.fctrYes 64.54013369
## Q115611.fctrYes 55.61206173
## Q98197.fctrNo 52.69690976
## Q109244.fctrNo:Q119650.fctrGiving 48.42576135
## Q116881.fctrHappy 46.09192006
## Q109244.fctrNo:Income.fctr.L 43.29279743
## Hhold.fctrPKn 41.17887794
## Q106997.fctrGr 39.52523209
## Q108342.fctrOnline 37.78484599
## Edn.fctr^7 36.29940771
## Q109244.fctrYes:Income.fctr.L 34.85072480
## Q120379.fctrNo 33.74249405
## Q109244.fctrNo:Income.fctr.C 32.06273684
## Q109244.fctrNo:Edn.fctr.L 30.88881371
## Q109244.fctrYes:Q116881.fctrHappy 29.38897891
## Q122771.fctrPt 28.00320961
## Q100689.fctrYes 26.86898299
## Q123621.fctrYes 25.62710047
## Q109244.fctrYes:Q123621.fctrYes 25.21567205
## Q121011.fctrNo 23.75398503
## Q101163.fctrDad 22.29207105
## Q109244.fctrYes:Q101163.fctrMom 21.27931275
## Q109244.fctrNo:Q119334.fctrYes 20.02571954
## Q124122.fctrYes 18.64516818
## Q121699.fctrYes 17.89699784
## Q122771.fctrPc 17.06512114
## Q104996.fctrYes 15.36384707
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.57949576
## Q108343.fctrYes 13.21156227
## Q102674.fctrYes 12.08112176
## YOB.Age.fctr(40,50]:YOB.Age.dff 10.93839173
## YOB.Age.fctr^7 9.40830535
## Q109244.fctrYes:Edn.fctr^7 8.86574323
## Q115611.fctrNo 7.37062918
## YOB.Age.fctr.L 6.57320444
## Q109244.fctrNo:YOB.Age.fctr^6 5.90465515
## Q109244.fctrNo:YOB.Age.fctr^7 4.91402305
## YOB.Age.fctr(25,30]:YOB.Age.dff 3.89746513
## Q109244.fctrYes:Q115611.fctrNo 3.25707597
## Q109244.fctrYes:.clusterid.fctr3 2.56126079
## Q109244.fctrYes:Edn.fctr.L 1.78716549
## Gender.fctrF 1.41987740
## Gender.fctrM 1.09707638
## Q100010.fctrYes 0.72555596
## Q100010.fctrNo 0.56730464
## Q100562.fctrNo 0.33475266
## Q100562.fctrYes 0.04896745
## Q100680.fctrNo 0.00000000
## Q100680.fctrYes 0.00000000
## Q101162.fctrOptimist 0.00000000
## Q101162.fctrPessimist 0.00000000
## Q101596.fctrNo 0.00000000
## Q101596.fctrYes 0.00000000
## Q102089.fctrOwn 0.00000000
## Q102089.fctrRent 0.00000000
## Q102289.fctrNo 0.00000000
## Q102289.fctrYes 0.00000000
## Q102687.fctrNo 0.00000000
## Q102687.fctrYes 0.00000000
## Q102906.fctrNo 0.00000000
## Q102906.fctrYes 0.00000000
## Q103293.fctrNo 0.00000000
## Q103293.fctrYes 0.00000000
## Q105655.fctrNo 0.00000000
## Q105655.fctrYes 0.00000000
## Q105840.fctrNo 0.00000000
## Q105840.fctrYes 0.00000000
## Q106042.fctrNo 0.00000000
## Q106042.fctrYes 0.00000000
## Q106272.fctrNo 0.00000000
## Q106272.fctrYes 0.00000000
## Q106388.fctrNo 0.00000000
## Q106388.fctrYes 0.00000000
## Q106389.fctrNo 0.00000000
## Q106389.fctrYes 0.00000000
## Q106993.fctrNo 0.00000000
## Q106993.fctrYes 0.00000000
## Q107491.fctrNo 0.00000000
## Q107491.fctrYes 0.00000000
## Q107869.fctrNo 0.00000000
## Q107869.fctrYes 0.00000000
## Q108617.fctrNo 0.00000000
## Q108617.fctrYes 0.00000000
## Q108754.fctrNo 0.00000000
## Q108754.fctrYes 0.00000000
## Q108855.fctrUmm... 0.00000000
## Q108855.fctrYes! 0.00000000
## Q108856.fctrSocialize 0.00000000
## Q108856.fctrSpace 0.00000000
## Q108950.fctrCautious 0.00000000
## Q108950.fctrRisk-friendly 0.00000000
## Q109244.fctrNo 0.00000000
## Edn.fctr.L 0.00000000
## Edn.fctr.Q 0.00000000
## Edn.fctr.C 0.00000000
## Edn.fctr^4 0.00000000
## Edn.fctr^5 0.00000000
## Edn.fctr^6 0.00000000
## Hhold.fctrMKn 0.00000000
## Hhold.fctrMKy 0.00000000
## Hhold.fctrPKy 0.00000000
## Hhold.fctrSKn 0.00000000
## Hhold.fctrSKy 0.00000000
## Income.fctr.L 0.00000000
## Income.fctr.Q 0.00000000
## Income.fctr.C 0.00000000
## Income.fctr^4 0.00000000
## Income.fctr^5 0.00000000
## Income.fctr^6 0.00000000
## Q100689.fctrNo 0.00000000
## Q101163.fctrMom 0.00000000
## Q102674.fctrNo 0.00000000
## Q104996.fctrNo 0.00000000
## Q106997.fctrYy 0.00000000
## Q108342.fctrIn-person 0.00000000
## Q108343.fctrNo 0.00000000
## Q113181.fctrNo 0.00000000
## Q113181.fctrYes 0.00000000
## Q116881.fctrRight 0.00000000
## Q119334.fctrNo 0.00000000
## Q119334.fctrYes 0.00000000
## Q119650.fctrGiving 0.00000000
## Q119650.fctrReceiving 0.00000000
## Q120379.fctrYes 0.00000000
## Q121699.fctrNo 0.00000000
## Q123621.fctrNo 0.00000000
## Q124122.fctrNo 0.00000000
## Q98197.fctrYes 0.00000000
## YOB.Age.fctr.Q 0.00000000
## YOB.Age.fctr.C 0.00000000
## YOB.Age.fctr^4 0.00000000
## YOB.Age.fctr^5 0.00000000
## YOB.Age.fctr^6 0.00000000
## YOB.Age.fctr^8 0.00000000
## Q109367.fctrNo 0.00000000
## Q109367.fctrYes 0.00000000
## Q110740.fctrMac 0.00000000
## Q110740.fctrPC 0.00000000
## Q111220.fctrNo 0.00000000
## Q111220.fctrYes 0.00000000
## Q111580.fctrDemanding 0.00000000
## Q111580.fctrSupportive 0.00000000
## Q111848.fctrNo 0.00000000
## Q111848.fctrYes 0.00000000
## Q112270.fctrNo 0.00000000
## Q112270.fctrYes 0.00000000
## Q112478.fctrNo 0.00000000
## Q112478.fctrYes 0.00000000
## Q112512.fctrNo 0.00000000
## Q112512.fctrYes 0.00000000
## Q113583.fctrTalk 0.00000000
## Q113583.fctrTunes 0.00000000
## Q113584.fctrPeople 0.00000000
## Q113584.fctrTechnology 0.00000000
## Q113992.fctrNo 0.00000000
## Q113992.fctrYes 0.00000000
## Q114152.fctrNo 0.00000000
## Q114152.fctrYes 0.00000000
## Q114386.fctrMysterious 0.00000000
## Q114386.fctrTMI 0.00000000
## Q114517.fctrNo 0.00000000
## Q114517.fctrYes 0.00000000
## Q114748.fctrNo 0.00000000
## Q114748.fctrYes 0.00000000
## Q114961.fctrNo 0.00000000
## Q114961.fctrYes 0.00000000
## Q115195.fctrNo 0.00000000
## Q115195.fctrYes 0.00000000
## Q115390.fctrNo 0.00000000
## Q115390.fctrYes 0.00000000
## Q115602.fctrNo 0.00000000
## Q115602.fctrYes 0.00000000
## Q115610.fctrNo 0.00000000
## Q115610.fctrYes 0.00000000
## Q115777.fctrEnd 0.00000000
## Q115777.fctrStart 0.00000000
## Q115899.fctrCs 0.00000000
## Q115899.fctrMe 0.00000000
## Q116197.fctrA.M. 0.00000000
## Q116197.fctrP.M. 0.00000000
## Q116441.fctrNo 0.00000000
## Q116441.fctrYes 0.00000000
## Q116448.fctrNo 0.00000000
## Q116448.fctrYes 0.00000000
## Q116601.fctrNo 0.00000000
## Q116601.fctrYes 0.00000000
## Q116797.fctrNo 0.00000000
## Q116797.fctrYes 0.00000000
## Q116953.fctrNo 0.00000000
## Q116953.fctrYes 0.00000000
## Q117186.fctrCoolheaded 0.00000000
## Q117186.fctrHotheaded 0.00000000
## Q117193.fctrOddhours 0.00000000
## Q117193.fctrStandardhours 0.00000000
## Q118117.fctrNo 0.00000000
## Q118117.fctrYes 0.00000000
## Q118232.fctrId 0.00000000
## Q118232.fctrPr 0.00000000
## Q118233.fctrNo 0.00000000
## Q118233.fctrYes 0.00000000
## Q118237.fctrNo 0.00000000
## Q118237.fctrYes 0.00000000
## Q118892.fctrNo 0.00000000
## Q118892.fctrYes 0.00000000
## Q119851.fctrNo 0.00000000
## Q119851.fctrYes 0.00000000
## Q120012.fctrNo 0.00000000
## Q120012.fctrYes 0.00000000
## Q120014.fctrNo 0.00000000
## Q120014.fctrYes 0.00000000
## Q120194.fctrStudyfirst 0.00000000
## Q120194.fctrTryfirst 0.00000000
## Q120472.fctrArt 0.00000000
## Q120472.fctrScience 0.00000000
## Q120650.fctrNo 0.00000000
## Q120650.fctrYes 0.00000000
## Q120978.fctrNo 0.00000000
## Q120978.fctrYes 0.00000000
## Q121011.fctrYes 0.00000000
## Q121700.fctrNo 0.00000000
## Q121700.fctrYes 0.00000000
## Q122120.fctrNo 0.00000000
## Q122120.fctrYes 0.00000000
## Q122769.fctrNo 0.00000000
## Q122769.fctrYes 0.00000000
## Q122770.fctrNo 0.00000000
## Q122770.fctrYes 0.00000000
## Q123464.fctrNo 0.00000000
## Q123464.fctrYes 0.00000000
## Q124742.fctrNo 0.00000000
## Q124742.fctrYes 0.00000000
## Q96024.fctrNo 0.00000000
## Q96024.fctrYes 0.00000000
## Q98078.fctrNo 0.00000000
## Q98078.fctrYes 0.00000000
## Q98578.fctrNo 0.00000000
## Q98578.fctrYes 0.00000000
## Q98869.fctrNo 0.00000000
## Q98869.fctrYes 0.00000000
## Q99480.fctrNo 0.00000000
## Q99480.fctrYes 0.00000000
## Q99581.fctrNo 0.00000000
## Q99581.fctrYes 0.00000000
## Q99716.fctrNo 0.00000000
## Q99716.fctrYes 0.00000000
## Q99982.fctrCheck! 0.00000000
## Q99982.fctrNope 0.00000000
## Q109244.fctrNo:Edn.fctr.Q 0.00000000
## Q109244.fctrYes:Edn.fctr.Q 0.00000000
## Q109244.fctrNo:Edn.fctr.C 0.00000000
## Q109244.fctrYes:Edn.fctr.C 0.00000000
## Q109244.fctrNo:Edn.fctr^4 0.00000000
## Q109244.fctrYes:Edn.fctr^4 0.00000000
## Q109244.fctrNo:Edn.fctr^5 0.00000000
## Q109244.fctrYes:Edn.fctr^5 0.00000000
## Q109244.fctrNo:Edn.fctr^6 0.00000000
## Q109244.fctrYes:Edn.fctr^6 0.00000000
## Q109244.fctrNo:Edn.fctr^7 0.00000000
## Q109244.fctrNo:Hhold.fctrMKn 0.00000000
## Q109244.fctrYes:Hhold.fctrMKn 0.00000000
## Q109244.fctrNo:Hhold.fctrMKy 0.00000000
## Q109244.fctrYes:Hhold.fctrMKy 0.00000000
## Q109244.fctrNo:Hhold.fctrPKn 0.00000000
## Q109244.fctrYes:Hhold.fctrPKn 0.00000000
## Q109244.fctrNo:Hhold.fctrPKy 0.00000000
## Q109244.fctrYes:Hhold.fctrPKy 0.00000000
## Q109244.fctrNo:Hhold.fctrSKn 0.00000000
## Q109244.fctrYes:Hhold.fctrSKn 0.00000000
## Q109244.fctrNo:Hhold.fctrSKy 0.00000000
## Q109244.fctrYes:Hhold.fctrSKy 0.00000000
## Q109244.fctrNo:Income.fctr.Q 0.00000000
## Q109244.fctrYes:Income.fctr.Q 0.00000000
## Q109244.fctrYes:Income.fctr.C 0.00000000
## Q109244.fctrNo:Income.fctr^4 0.00000000
## Q109244.fctrYes:Income.fctr^4 0.00000000
## Q109244.fctrNo:Income.fctr^5 0.00000000
## Q109244.fctrYes:Income.fctr^5 0.00000000
## Q109244.fctrNo:Income.fctr^6 0.00000000
## Q109244.fctrYes:Income.fctr^6 0.00000000
## Q109244.fctrNo:Q100689.fctrNo 0.00000000
## Q109244.fctrYes:Q100689.fctrNo 0.00000000
## Q109244.fctrNo:Q100689.fctrYes 0.00000000
## Q109244.fctrYes:Q100689.fctrYes 0.00000000
## Q109244.fctrNo:Q101163.fctrDad 0.00000000
## Q109244.fctrYes:Q101163.fctrDad 0.00000000
## Q109244.fctrNo:Q101163.fctrMom 0.00000000
## Q109244.fctrNo:Q102674.fctrNo 0.00000000
## Q109244.fctrYes:Q102674.fctrNo 0.00000000
## Q109244.fctrNo:Q102674.fctrYes 0.00000000
## Q109244.fctrYes:Q102674.fctrYes 0.00000000
## Q109244.fctrNo:Q104996.fctrNo 0.00000000
## Q109244.fctrYes:Q104996.fctrNo 0.00000000
## Q109244.fctrNo:Q104996.fctrYes 0.00000000
## Q109244.fctrYes:Q104996.fctrYes 0.00000000
## Q109244.fctrNo:Q106997.fctrGr 0.00000000
## Q109244.fctrYes:Q106997.fctrGr 0.00000000
## Q109244.fctrNo:Q106997.fctrYy 0.00000000
## Q109244.fctrYes:Q106997.fctrYy 0.00000000
## Q109244.fctrNo:Q108342.fctrIn-person 0.00000000
## Q109244.fctrYes:Q108342.fctrIn-person 0.00000000
## Q109244.fctrNo:Q108342.fctrOnline 0.00000000
## Q109244.fctrYes:Q108342.fctrOnline 0.00000000
## Q109244.fctrNo:Q108343.fctrNo 0.00000000
## Q109244.fctrYes:Q108343.fctrNo 0.00000000
## Q109244.fctrNo:Q108343.fctrYes 0.00000000
## Q109244.fctrYes:Q108343.fctrYes 0.00000000
## Q109244.fctrNo:Q113181.fctrNo 0.00000000
## Q109244.fctrYes:Q113181.fctrNo 0.00000000
## Q109244.fctrYes:Q113181.fctrYes 0.00000000
## Q109244.fctrNo:Q115611.fctrNo 0.00000000
## Q109244.fctrNo:Q115611.fctrYes 0.00000000
## Q109244.fctrYes:Q115611.fctrYes 0.00000000
## Q109244.fctrNo:Q116881.fctrHappy 0.00000000
## Q109244.fctrNo:Q116881.fctrRight 0.00000000
## Q109244.fctrYes:Q116881.fctrRight 0.00000000
## Q109244.fctrNo:Q119334.fctrNo 0.00000000
## Q109244.fctrYes:Q119334.fctrNo 0.00000000
## Q109244.fctrYes:Q119334.fctrYes 0.00000000
## Q109244.fctrYes:Q119650.fctrGiving 0.00000000
## Q109244.fctrNo:Q119650.fctrReceiving 0.00000000
## Q109244.fctrYes:Q119650.fctrReceiving 0.00000000
## Q109244.fctrNo:Q120379.fctrNo 0.00000000
## Q109244.fctrYes:Q120379.fctrNo 0.00000000
## Q109244.fctrNo:Q120379.fctrYes 0.00000000
## Q109244.fctrYes:Q120379.fctrYes 0.00000000
## Q109244.fctrNo:Q121699.fctrNo 0.00000000
## Q109244.fctrYes:Q121699.fctrNo 0.00000000
## Q109244.fctrNo:Q121699.fctrYes 0.00000000
## Q109244.fctrYes:Q121699.fctrYes 0.00000000
## Q109244.fctrNo:Q122771.fctrPc 0.00000000
## Q109244.fctrYes:Q122771.fctrPc 0.00000000
## Q109244.fctrNo:Q122771.fctrPt 0.00000000
## Q109244.fctrYes:Q122771.fctrPt 0.00000000
## Q109244.fctrNo:Q123621.fctrNo 0.00000000
## Q109244.fctrYes:Q123621.fctrNo 0.00000000
## Q109244.fctrNo:Q123621.fctrYes 0.00000000
## Q109244.fctrNo:Q124122.fctrNo 0.00000000
## Q109244.fctrYes:Q124122.fctrNo 0.00000000
## Q109244.fctrNo:Q124122.fctrYes 0.00000000
## Q109244.fctrYes:Q124122.fctrYes 0.00000000
## Q109244.fctrNo:Q98197.fctrNo 0.00000000
## Q109244.fctrYes:Q98197.fctrNo 0.00000000
## Q109244.fctrNo:Q98197.fctrYes 0.00000000
## Q109244.fctrYes:Q98197.fctrYes 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.L 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.L 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.Q 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.Q 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.C 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.C 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^4 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^4 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^5 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^5 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^6 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^7 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^8 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^8 0.00000000
## Q109244.fctrNA:.clusterid.fctr2 0.00000000
## Q109244.fctrNo:.clusterid.fctr2 0.00000000
## Q109244.fctrYes:.clusterid.fctr2 0.00000000
## Q109244.fctrNA:.clusterid.fctr3 0.00000000
## Q109244.fctrNo:.clusterid.fctr3 0.00000000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000000#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
if (grepl("pca", myparseMdlId(mdl_id)$preProc, fixed = TRUE)) {
indepVar <- unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], ","))
vectorizedObsMtx <- myget_vectorized_obs_df(obs_df, rsp_var = glb_rsp_var,
indep_vars = indepVar)
if (!inherits(vectorizedObsMtx, "matrix")) {
vectorizedObsMtx[, glb_rsp_var] <- as.numeric(vectorizedObsMtx[, glb_rsp_var])
vectorizedObsMtx <- as.matrix(vectorizedObsMtx)
}
rotationMtx <- glb_models_lst[[mdl_id]]$preProcess$rotation
pcaobs_df <- as.data.frame(vectorizedObsMtx[, dimnames(rotationMtx)[[1]]] %*%
rotationMtx)
# pcaobs_df[, glb_rsp_var] <- obs_df[, glb_rsp_var]
# pcaobs_df[, rsp_var_out] <- obs_df[, rsp_var_out]
# pcaobs_df[, glbFeatsId] <- obs_df[, glbFeatsId]
obs_df <- cbind(obs_df, pcaobs_df)
}
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
require(lazyeval)
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important") else
print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 129
## Loading required package: lazyeval
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.RFE.X.Inc..rcv.bagEarth.prob
## 1 3895 R 0.03265932
## 2 1236 R 0.04304825
## 3 2749 R 0.04387413
## 4 3212 R 0.05387724
## 5 4506 R 0.05701378
## 6 1515 R 0.05737857
## Party.fctr.RFE.X.Inc..rcv.bagEarth
## 1 D
## 2 D
## 3 D
## 4 D
## 5 D
## 6 D
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err.abs
## 1 0.9673407
## 2 0.9569518
## 3 0.9561259
## 4 0.9461228
## 5 0.9429862
## 6 0.9426214
## Party.fctr.RFE.X.Inc..rcv.bagEarth.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.error
## 1 -0.5673407
## 2 -0.5569518
## 3 -0.5561259
## 4 -0.5461228
## 5 -0.5429862
## 6 -0.5426214
## USER_ID Party.fctr Party.fctr.RFE.X.Inc..rcv.bagEarth.prob
## 104 535 R 0.3349249
## 210 4970 R 0.4853982
## 268 290 R 0.5245367
## 384 42 D 0.6254853
## 428 4061 D 0.7184765
## 431 1660 D 0.7270814
## Party.fctr.RFE.X.Inc..rcv.bagEarth
## 104 D
## 210 D
## 268 D
## 384 R
## 428 R
## 431 R
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err
## 104 TRUE
## 210 TRUE
## 268 TRUE
## 384 TRUE
## 428 TRUE
## 431 TRUE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err.abs
## 104 0.6650751
## 210 0.5146018
## 268 0.4754633
## 384 0.6254853
## 428 0.7184765
## 431 0.7270814
## Party.fctr.RFE.X.Inc..rcv.bagEarth.is.acc
## 104 FALSE
## 210 FALSE
## 268 FALSE
## 384 FALSE
## 428 FALSE
## 431 FALSE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.accurate
## 104 FALSE
## 210 FALSE
## 268 FALSE
## 384 FALSE
## 428 FALSE
## 431 FALSE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.error
## 104 -0.26507506
## 210 -0.11460181
## 268 -0.07546328
## 384 0.02548528
## 428 0.11847648
## 431 0.12708137
## USER_ID Party.fctr Party.fctr.RFE.X.Inc..rcv.bagEarth.prob
## 459 4691 D 0.8148580
## 460 1309 D 0.8208309
## 461 3978 D 0.8390278
## 462 892 D 0.8542985
## 463 217 D 0.8765461
## 464 3006 D 0.8977183
## Party.fctr.RFE.X.Inc..rcv.bagEarth
## 459 R
## 460 R
## 461 R
## 462 R
## 463 R
## 464 R
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err
## 459 TRUE
## 460 TRUE
## 461 TRUE
## 462 TRUE
## 463 TRUE
## 464 TRUE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err.abs
## 459 0.8148580
## 460 0.8208309
## 461 0.8390278
## 462 0.8542985
## 463 0.8765461
## 464 0.8977183
## Party.fctr.RFE.X.Inc..rcv.bagEarth.is.acc
## 459 FALSE
## 460 FALSE
## 461 FALSE
## 462 FALSE
## 463 FALSE
## 464 FALSE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.accurate
## 459 FALSE
## 460 FALSE
## 461 FALSE
## 462 FALSE
## 463 FALSE
## 464 FALSE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.error
## 459 0.2148580
## 460 0.2208309
## 461 0.2390278
## 462 0.2542985
## 463 0.2765461
## 464 0.2977183
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## Q109244.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## NA NA 438 1746 547 0.3920952 0.3928251
## No No 498 1961 622 0.4403773 0.4466368
## Yes Yes 179 746 223 0.1675275 0.1605381
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## NA 0.3929598 814.9300 0.4667411 1746 208.8033
## No 0.4468391 855.6562 0.4363366 1961 236.6868
## Yes 0.1602011 161.9998 0.2171579 746 84.8316
## err.abs.OOB.mean
## NA 0.4767199
## No 0.4752746
## Yes 0.4739195
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1115.000000 4453.000000 1392.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 1832.585921 1.120236
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 4453.000000 530.321671 1.425914write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 311.895 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 fit.models 1 1 1 261.688 311.903 50.215
## 3 fit.models 1 2 2 311.904 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 3 fit.models 1 2 2 311.904 316.217
## 4 fit.data.training 2 0 0 316.218 NA
## elapsed
## 3 4.313
## 4 NAStep 2.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var]))) {
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (myparseMdlId(glbMdlSelId)$family == "RFE.X") {
indepVar <- mygetIndepVar(glb_feats_df)
trnRFEResults <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(trnRFEResults)),
sort(predictors(glbRFEResults))))) {
print("Diffs predictors(trnRFEResults) vs. predictors(glbRFEResults):")
print(setdiff(predictors(trnRFEResults), predictors(glbRFEResults)))
print("Diffs predictors(glbRFEResults) vs. predictors(trnRFEResults):")
print(setdiff(predictors(glbRFEResults), predictors(trnRFEResults)))
}
}
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(trnRFEResults))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
# indepVar <- myextract_actual_feats(predictors(trnRFEResults))
indepVar <- myextract_actual_feats(predictors(glbRFEResults))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
# if (!is.null(glbMdlPreprocMethods) &&
# ((match_pos <- regexpr(gsub(".", "\\.",
# paste(glbMdlPreprocMethods, collapse = "|"),
# fixed = TRUE), glbMdlSelId)) != -1))
# ths_preProcess <- str_sub(glbMdlSelId, match_pos,
# match_pos + attr(match_pos, "match.length") - 1) else
# ths_preProcess <- NULL
# mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
# "Final.Ensemble", "Final")
mdl_id_pfx <- paste("Final", myparseMdlId(glbMdlSelId)$family, sep = ".")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
mdl_specs_lst <- myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = myparseMdlId(glbMdlSelId)$preProcess))
ret_lst <-
myfit_mdl(mdl_specs_lst = mdl_specs_lst,
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glbMdlFinId <- mdl_specs_lst$id
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
}
}
}## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.RFE.X.Inc##rcv#bagEarth"
## [1] " indepVar: Q109244.fctr,Q115611.fctr,Q113181.fctr,Gender.fctr,Q98197.fctr,Q101163.fctr,Q120379.fctr,Hhold.fctr,Q105840.fctr,.clusterid.fctr,Q120472.fctr,Q106272.fctr,Q115899.fctr,Q119851.fctr,Q99480.fctr,Q106042.fctr,Q98869.fctr,Q101596.fctr,Q110740.fctr,Q102089.fctr,Q120014.fctr,Q120650.fctr,Q108855.fctr,Edn.fctr,Q118892.fctr,Q116881.fctr,Q100680.fctr,Q108342.fctr,Q107869.fctr,Q112478.fctr,Q115195.fctr,Q121699.fctr,Q106388.fctr,Q106389.fctr,Q120012.fctr,Q116448.fctr,Q119650.fctr,Q96024.fctr,Q118237.fctr,Income.fctr,Q123621.fctr,Q108617.fctr,Q113583.fctr,Q118232.fctr,Q120194.fctr,Q122770.fctr,Q121700.fctr,Q124122.fctr,Q114152.fctr,Q122771.fctr,Q106997.fctr,Q98078.fctr,Q112270.fctr,Q116953.fctr,Q111848.fctr,Q100689.fctr,Q118233.fctr,Q99982.fctr,Q106993.fctr,Q115390.fctr,Q102906.fctr,Q123464.fctr,Q116797.fctr,Q104996.fctr,Q109367.fctr,Q111220.fctr,Q99716.fctr,Q117186.fctr,Q102674.fctr,Q118117.fctr,Q122769.fctr,Q108754.fctr,Q121011.fctr,Q116441.fctr,YOB.Age.fctr,Q105655.fctr,Q108856.fctr,Q120978.fctr,Q101162.fctr,Q119334.fctr,Q102289.fctr,Q111580.fctr,Q112512.fctr,Q99581.fctr,Q122120.fctr,Q116197.fctr,Q114386.fctr,Q117193.fctr,Q114961.fctr,Q115777.fctr,YOB.Age.dff,Q100010.fctr,Q113584.fctr,Q98578.fctr,Q102687.fctr,Q100562.fctr,Q116601.fctr,Q103293.fctr,Q108950.fctr,Q115602.fctr,Q107491.fctr,Q114748.fctr,Q114517.fctr,Q115610.fctr,Q124742.fctr,Q108343.fctr,Q113992.fctr"
## [1] "myfit_mdl: setup complete: 0.779000 secs"
## Fitting nprune = 256, degree = 1 on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## [1] "myfit_mdl: train complete: 414.363000 secs"
## nprune degree
## 1 256 1
## [1] "myprint_mdl: B:"
## [1] 50
## [1] "myprint_mdl: oob:"
## Accuracy Kappa
## [1,] 0.6059425 0.2119092
## [2,] 0.6060752 0.2080410
## [3,] 0.6244520 0.2490475
## [4,] 0.6034568 0.2044055
## [5,] 0.6204022 0.2341223
## [6,] 0.6225490 0.2391695
## [7,] 0.6151560 0.2267041
## [8,] 0.6188862 0.2326794
## [9,] 0.6172899 0.2283086
## [10,] 0.6185721 0.2323399
## [11,] 0.6171875 0.2297317
## [12,] 0.6125908 0.2246155
## [13,] 0.6092397 0.2146088
## [14,] 0.6128718 0.2211584
## [15,] 0.6025949 0.2014371
## [16,] 0.6137132 0.2196104
## [17,] 0.6058968 0.2088575
## [18,] 0.6068627 0.2097392
## [19,] 0.6113022 0.2203877
## [20,] 0.6208554 0.2358768
## [21,] 0.6198347 0.2380016
## [22,] 0.6236145 0.2409751
## [23,] 0.6059570 0.2102420
## [24,] 0.5995146 0.1936708
## [25,] 0.6154985 0.2259659
## [26,] 0.6135809 0.2234934
## [27,] 0.6037549 0.2049823
## [28,] 0.6121359 0.2181922
## [29,] 0.6174040 0.2280844
## [30,] 0.6099951 0.2156844
## [31,] 0.6253659 0.2477014
## [32,] 0.6139397 0.2205731
## [33,] 0.6168133 0.2313725
## [34,] 0.6103332 0.2180492
## [35,] 0.6323383 0.2558263
## [36,] 0.6013645 0.1988537
## [37,] 0.6078240 0.2148231
## [38,] 0.6055313 0.2068631
## [39,] 0.6161021 0.2263856
## [40,] 0.6388482 0.2708760
## [41,] 0.6037365 0.2043412
## [42,] 0.6003889 0.2008917
## [43,] 0.6094059 0.2136223
## [44,] 0.6267640 0.2502808
## [45,] 0.5999037 0.1951313
## [46,] 0.6008708 0.1980019
## [47,] 0.6058680 0.2049380
## [48,] 0.6024631 0.1983572
## [49,] 0.6276382 0.2460109
## [50,] 0.6167076 0.2278963
## [1] "myprint_mdl: levels:"
## [1] "D" "R"
## [1] "myprint_mdl: weights:"
## [1] TRUE
## [1] "myprint_mdl: xNames:"
## [1] ".clusterid.fctr2" ".clusterid.fctr3"
## [3] "Edn.fctr.L" "Edn.fctr.Q"
## [5] "Edn.fctr.C" "Edn.fctr^4"
## [7] "Edn.fctr^5" "Edn.fctr^6"
## [9] "Edn.fctr^7" "Gender.fctrF"
## [11] "Gender.fctrM" "Hhold.fctrMKn"
## [13] "Hhold.fctrMKy" "Hhold.fctrPKn"
## [15] "Hhold.fctrPKy" "Hhold.fctrSKn"
## [17] "Hhold.fctrSKy" "Income.fctr.L"
## [19] "Income.fctr.Q" "Income.fctr.C"
## [21] "Income.fctr^4" "Income.fctr^5"
## [23] "Income.fctr^6" "Q100010.fctrNo"
## [25] "Q100010.fctrYes" "Q100562.fctrNo"
## [27] "Q100562.fctrYes" "Q100680.fctrNo"
## [29] "Q100680.fctrYes" "Q100689.fctrNo"
## [31] "Q100689.fctrYes" "Q101162.fctrOptimist"
## [33] "Q101162.fctrPessimist" "Q101163.fctrDad"
## [35] "Q101163.fctrMom" "Q101596.fctrNo"
## [37] "Q101596.fctrYes" "Q102089.fctrOwn"
## [39] "Q102089.fctrRent" "Q102289.fctrNo"
## [41] "Q102289.fctrYes" "Q102674.fctrNo"
## [43] "Q102674.fctrYes" "Q102687.fctrNo"
## [45] "Q102687.fctrYes" "Q102906.fctrNo"
## [47] "Q102906.fctrYes" "Q103293.fctrNo"
## [49] "Q103293.fctrYes" "Q104996.fctrNo"
## [51] "Q104996.fctrYes" "Q105655.fctrNo"
## [53] "Q105655.fctrYes" "Q105840.fctrNo"
## [55] "Q105840.fctrYes" "Q106042.fctrNo"
## [57] "Q106042.fctrYes" "Q106272.fctrNo"
## [59] "Q106272.fctrYes" "Q106388.fctrNo"
## [61] "Q106388.fctrYes" "Q106389.fctrNo"
## [63] "Q106389.fctrYes" "Q106993.fctrNo"
## [65] "Q106993.fctrYes" "Q106997.fctrGr"
## [67] "Q106997.fctrYy" "Q107491.fctrNo"
## [69] "Q107491.fctrYes" "Q107869.fctrNo"
## [71] "Q107869.fctrYes" "Q108342.fctrIn-person"
## [73] "Q108342.fctrOnline" "Q108343.fctrNo"
## [75] "Q108343.fctrYes" "Q108617.fctrNo"
## [77] "Q108617.fctrYes" "Q108754.fctrNo"
## [79] "Q108754.fctrYes" "Q108855.fctrUmm..."
## [81] "Q108855.fctrYes!" "Q108856.fctrSocialize"
## [83] "Q108856.fctrSpace" "Q108950.fctrCautious"
## [85] "Q108950.fctrRisk-friendly" "Q109244.fctrNo"
## [87] "Q109244.fctrYes" "Q109367.fctrNo"
## [89] "Q109367.fctrYes" "Q110740.fctrMac"
## [91] "Q110740.fctrPC" "Q111220.fctrNo"
## [93] "Q111220.fctrYes" "Q111580.fctrDemanding"
## [95] "Q111580.fctrSupportive" "Q111848.fctrNo"
## [97] "Q111848.fctrYes" "Q112270.fctrNo"
## [99] "Q112270.fctrYes" "Q112478.fctrNo"
## [101] "Q112478.fctrYes" "Q112512.fctrNo"
## [103] "Q112512.fctrYes" "Q113181.fctrNo"
## [105] "Q113181.fctrYes" "Q113583.fctrTalk"
## [107] "Q113583.fctrTunes" "Q113584.fctrPeople"
## [109] "Q113584.fctrTechnology" "Q113992.fctrNo"
## [111] "Q113992.fctrYes" "Q114152.fctrNo"
## [113] "Q114152.fctrYes" "Q114386.fctrMysterious"
## [115] "Q114386.fctrTMI" "Q114517.fctrNo"
## [117] "Q114517.fctrYes" "Q114748.fctrNo"
## [119] "Q114748.fctrYes" "Q114961.fctrNo"
## [121] "Q114961.fctrYes" "Q115195.fctrNo"
## [123] "Q115195.fctrYes" "Q115390.fctrNo"
## [125] "Q115390.fctrYes" "Q115602.fctrNo"
## [127] "Q115602.fctrYes" "Q115610.fctrNo"
## [129] "Q115610.fctrYes" "Q115611.fctrNo"
## [131] "Q115611.fctrYes" "Q115777.fctrEnd"
## [133] "Q115777.fctrStart" "Q115899.fctrCs"
## [135] "Q115899.fctrMe" "Q116197.fctrA.M."
## [137] "Q116197.fctrP.M." "Q116441.fctrNo"
## [139] "Q116441.fctrYes" "Q116448.fctrNo"
## [141] "Q116448.fctrYes" "Q116601.fctrNo"
## [143] "Q116601.fctrYes" "Q116797.fctrNo"
## [145] "Q116797.fctrYes" "Q116881.fctrHappy"
## [147] "Q116881.fctrRight" "Q116953.fctrNo"
## [149] "Q116953.fctrYes" "Q117186.fctrCool headed"
## [151] "Q117186.fctrHot headed" "Q117193.fctrOdd hours"
## [153] "Q117193.fctrStandard hours" "Q118117.fctrNo"
## [155] "Q118117.fctrYes" "Q118232.fctrId"
## [157] "Q118232.fctrPr" "Q118233.fctrNo"
## [159] "Q118233.fctrYes" "Q118237.fctrNo"
## [161] "Q118237.fctrYes" "Q118892.fctrNo"
## [163] "Q118892.fctrYes" "Q119334.fctrNo"
## [165] "Q119334.fctrYes" "Q119650.fctrGiving"
## [167] "Q119650.fctrReceiving" "Q119851.fctrNo"
## [169] "Q119851.fctrYes" "Q120012.fctrNo"
## [171] "Q120012.fctrYes" "Q120014.fctrNo"
## [173] "Q120014.fctrYes" "Q120194.fctrStudy first"
## [175] "Q120194.fctrTry first" "Q120379.fctrNo"
## [177] "Q120379.fctrYes" "Q120472.fctrArt"
## [179] "Q120472.fctrScience" "Q120650.fctrNo"
## [181] "Q120650.fctrYes" "Q120978.fctrNo"
## [183] "Q120978.fctrYes" "Q121011.fctrNo"
## [185] "Q121011.fctrYes" "Q121699.fctrNo"
## [187] "Q121699.fctrYes" "Q121700.fctrNo"
## [189] "Q121700.fctrYes" "Q122120.fctrNo"
## [191] "Q122120.fctrYes" "Q122769.fctrNo"
## [193] "Q122769.fctrYes" "Q122770.fctrNo"
## [195] "Q122770.fctrYes" "Q122771.fctrPc"
## [197] "Q122771.fctrPt" "Q123464.fctrNo"
## [199] "Q123464.fctrYes" "Q123621.fctrNo"
## [201] "Q123621.fctrYes" "Q124122.fctrNo"
## [203] "Q124122.fctrYes" "Q124742.fctrNo"
## [205] "Q124742.fctrYes" "Q96024.fctrNo"
## [207] "Q96024.fctrYes" "Q98078.fctrNo"
## [209] "Q98078.fctrYes" "Q98197.fctrNo"
## [211] "Q98197.fctrYes" "Q98578.fctrNo"
## [213] "Q98578.fctrYes" "Q98869.fctrNo"
## [215] "Q98869.fctrYes" "Q99480.fctrNo"
## [217] "Q99480.fctrYes" "Q99581.fctrNo"
## [219] "Q99581.fctrYes" "Q99716.fctrNo"
## [221] "Q99716.fctrYes" "Q99982.fctrCheck!"
## [223] "Q99982.fctrNope" "YOB.Age.dff"
## [225] "YOB.Age.fctr.L" "YOB.Age.fctr.Q"
## [227] "YOB.Age.fctr.C" "YOB.Age.fctr^4"
## [229] "YOB.Age.fctr^5" "YOB.Age.fctr^6"
## [231] "YOB.Age.fctr^7" "YOB.Age.fctr^8"
## [1] "myprint_mdl: problemType:"
## [1] "Classification"
## [1] "myprint_mdl: tuneValue:"
## nprune degree
## 1 256 1
## [1] "myprint_mdl: obsLevels:"
## [1] "D" "R"
## [1] "myfit_mdl: train diagnostics complete: 414.368000 secs"## Loading required namespace: pROC## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:plotrix':
##
## plotCI## The following object is masked from 'package:stats':
##
## lowess## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk
## Prediction
## Reference D R
## D 2086 865
## R 1057 1560
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.548132e-01 3.042446e-01 6.421571e-01 6.673069e-01 5.299928e-01
## AccuracyPValue McnemarPValue
## 1.625218e-79 1.320440e-05
## [1] "myfit_mdl: predict complete: 442.965000 secs"
## id
## 1 Final.RFE.X.Inc##rcv#bagEarth
## feats
## 1 Q109244.fctr,Q115611.fctr,Q113181.fctr,Gender.fctr,Q98197.fctr,Q101163.fctr,Q120379.fctr,Hhold.fctr,Q105840.fctr,.clusterid.fctr,Q120472.fctr,Q106272.fctr,Q115899.fctr,Q119851.fctr,Q99480.fctr,Q106042.fctr,Q98869.fctr,Q101596.fctr,Q110740.fctr,Q102089.fctr,Q120014.fctr,Q120650.fctr,Q108855.fctr,Edn.fctr,Q118892.fctr,Q116881.fctr,Q100680.fctr,Q108342.fctr,Q107869.fctr,Q112478.fctr,Q115195.fctr,Q121699.fctr,Q106388.fctr,Q106389.fctr,Q120012.fctr,Q116448.fctr,Q119650.fctr,Q96024.fctr,Q118237.fctr,Income.fctr,Q123621.fctr,Q108617.fctr,Q113583.fctr,Q118232.fctr,Q120194.fctr,Q122770.fctr,Q121700.fctr,Q124122.fctr,Q114152.fctr,Q122771.fctr,Q106997.fctr,Q98078.fctr,Q112270.fctr,Q116953.fctr,Q111848.fctr,Q100689.fctr,Q118233.fctr,Q99982.fctr,Q106993.fctr,Q115390.fctr,Q102906.fctr,Q123464.fctr,Q116797.fctr,Q104996.fctr,Q109367.fctr,Q111220.fctr,Q99716.fctr,Q117186.fctr,Q102674.fctr,Q118117.fctr,Q122769.fctr,Q108754.fctr,Q121011.fctr,Q116441.fctr,YOB.Age.fctr,Q105655.fctr,Q108856.fctr,Q120978.fctr,Q101162.fctr,Q119334.fctr,Q102289.fctr,Q111580.fctr,Q112512.fctr,Q99581.fctr,Q122120.fctr,Q116197.fctr,Q114386.fctr,Q117193.fctr,Q114961.fctr,Q115777.fctr,YOB.Age.dff,Q100010.fctr,Q113584.fctr,Q98578.fctr,Q102687.fctr,Q100562.fctr,Q116601.fctr,Q103293.fctr,Q108950.fctr,Q115602.fctr,Q107491.fctr,Q114748.fctr,Q114517.fctr,Q115610.fctr,Q124742.fctr,Q108343.fctr,Q113992.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 413.474 411.799
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6514907 0.706879 0.5961024 0.7158097
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6188021 0.6548132
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6421571 0.6673069 0.3042446
## [1] "myfit_mdl: exit: 442.996000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 4 fit.data.training 2 0 0 316.218 759.833
## 5 fit.data.training 2 1 1 759.834 NA
## elapsed
## 4 443.616
## 5 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.6glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## RFE.X.Inc..rcv.bagEarth.imp
## Q109244.fctrYes 100.00000000
## Q115611.fctrYes 55.61206173
## Q109244.fctrNo 0.00000000
## Q98197.fctrNo 52.69690976
## Q118232.fctrId 0.00000000
## Q116881.fctrRight 0.00000000
## Hhold.fctrPKn 41.17887794
## Q101163.fctrMom 0.00000000
## Income.fctr.L 0.00000000
## Q108855.fctrYes! 0.00000000
## Q120379.fctrYes 0.00000000
## Q120472.fctrScience 0.00000000
## Q115390.fctrYes 0.00000000
## Q99480.fctrYes 0.00000000
## Q116953.fctrNo 0.00000000
## Q106993.fctrYes 0.00000000
## Hhold.fctrSKn 0.00000000
## YOB.Age.dff NA
## YOB.Age.fctr.L 6.57320444
## Q98869.fctrNo 0.00000000
## Q111220.fctrNo 0.00000000
## Edn.fctr^7 36.29940771
## Q105655.fctrYes 0.00000000
## YOB.Age.fctr^6 0.00000000
## Q102674.fctrYes 12.08112176
## Q98197.fctrYes 0.00000000
## Q115899.fctrMe 0.00000000
## .clusterid.fctr2 NA
## .clusterid.fctr3 NA
## Edn.fctr.L 0.00000000
## Edn.fctr.Q 0.00000000
## Edn.fctr.C 0.00000000
## Edn.fctr^4 0.00000000
## Edn.fctr^5 0.00000000
## Edn.fctr^6 0.00000000
## Gender.fctrF 1.41987740
## Gender.fctrM 1.09707638
## Hhold.fctrMKn 0.00000000
## Hhold.fctrMKy 0.00000000
## Hhold.fctrPKy 0.00000000
## Hhold.fctrSKy 0.00000000
## Income.fctr.C 0.00000000
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## Income.fctr^4 0.00000000
## Income.fctr^5 0.00000000
## Income.fctr^6 0.00000000
## Q100010.fctrNo 0.56730464
## Q100010.fctrYes 0.72555596
## Q100562.fctrNo 0.33475266
## Q100562.fctrYes 0.04896745
## Q100680.fctrNo 0.00000000
## Q100680.fctrYes 0.00000000
## Q100689.fctrNo 0.00000000
## Q100689.fctrYes 26.86898299
## Q101162.fctrOptimist 0.00000000
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## Q101163.fctrDad 22.29207105
## Q101596.fctrNo 0.00000000
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## Q102089.fctrOwn 0.00000000
## Q102089.fctrRent 0.00000000
## Q102289.fctrNo 0.00000000
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## Q102674.fctrNo 0.00000000
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## Q102906.fctrNo 0.00000000
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## Q103293.fctrNo 0.00000000
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## Q104996.fctrNo 0.00000000
## Q104996.fctrYes 15.36384707
## Q105655.fctrNo 0.00000000
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## Q106997.fctrYy 0.00000000
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## Q107869.fctrNo 0.00000000
## Q107869.fctrYes 0.00000000
## Q108342.fctrIn-person 0.00000000
## Q108342.fctrOnline 37.78484599
## Q108343.fctrNo 0.00000000
## Q108343.fctrYes 13.21156227
## Q108617.fctrNo 0.00000000
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## Q108754.fctrNo 0.00000000
## Q108754.fctrYes 0.00000000
## Q108855.fctrUmm... 0.00000000
## Q108856.fctrSocialize 0.00000000
## Q108856.fctrSpace 0.00000000
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## Q108950.fctrRisk-friendly 0.00000000
## Q109367.fctrNo 0.00000000
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## Q110740.fctrMac 0.00000000
## Q110740.fctrPC 0.00000000
## Q111220.fctrYes 0.00000000
## Q111580.fctrDemanding 0.00000000
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## Q112270.fctrNo 0.00000000
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## Q112478.fctrYes 0.00000000
## Q112512.fctrNo 0.00000000
## Q112512.fctrYes 0.00000000
## Q113181.fctrNo 0.00000000
## Q113181.fctrYes 0.00000000
## Q113583.fctrTalk 0.00000000
## Q113583.fctrTunes 0.00000000
## Q113584.fctrPeople 0.00000000
## Q113584.fctrTechnology 0.00000000
## Q113992.fctrNo 0.00000000
## Q113992.fctrYes 0.00000000
## Q114152.fctrNo 0.00000000
## Q114152.fctrYes 0.00000000
## Q114386.fctrMysterious 0.00000000
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## Q114517.fctrNo 0.00000000
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## Q114748.fctrNo 0.00000000
## Q114748.fctrYes 0.00000000
## Q114961.fctrNo 0.00000000
## Q114961.fctrYes 0.00000000
## Q115195.fctrNo 0.00000000
## Q115195.fctrYes 0.00000000
## Q115390.fctrNo 0.00000000
## Q115602.fctrNo 0.00000000
## Q115602.fctrYes 0.00000000
## Q115610.fctrNo 0.00000000
## Q115610.fctrYes 0.00000000
## Q115611.fctrNo 7.37062918
## Q115777.fctrEnd 0.00000000
## Q115777.fctrStart 0.00000000
## Q115899.fctrCs 0.00000000
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## Q116441.fctrNo 0.00000000
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## Q116448.fctrNo 0.00000000
## Q116448.fctrYes 0.00000000
## Q116601.fctrNo 0.00000000
## Q116601.fctrYes 0.00000000
## Q116797.fctrNo 0.00000000
## Q116797.fctrYes 0.00000000
## Q116881.fctrHappy 46.09192006
## Q116953.fctrYes 0.00000000
## Q117186.fctrCoolheaded 0.00000000
## Q117186.fctrHotheaded 0.00000000
## Q117193.fctrOddhours 0.00000000
## Q117193.fctrStandardhours 0.00000000
## Q118117.fctrNo 0.00000000
## Q118117.fctrYes 0.00000000
## Q118232.fctrPr 0.00000000
## Q118233.fctrNo 0.00000000
## Q118233.fctrYes 0.00000000
## Q118237.fctrNo 0.00000000
## Q118237.fctrYes 0.00000000
## Q118892.fctrNo 0.00000000
## Q118892.fctrYes 0.00000000
## Q119334.fctrNo 0.00000000
## Q119334.fctrYes 0.00000000
## Q119650.fctrGiving 0.00000000
## Q119650.fctrReceiving 0.00000000
## Q119851.fctrNo 0.00000000
## Q119851.fctrYes 0.00000000
## Q120012.fctrNo 0.00000000
## Q120012.fctrYes 0.00000000
## Q120014.fctrNo 0.00000000
## Q120014.fctrYes 0.00000000
## Q120194.fctrStudyfirst 0.00000000
## Q120194.fctrTryfirst 0.00000000
## Q120379.fctrNo 33.74249405
## Q120472.fctrArt 0.00000000
## Q120650.fctrNo 0.00000000
## Q120650.fctrYes 0.00000000
## Q120978.fctrNo 0.00000000
## Q120978.fctrYes 0.00000000
## Q121011.fctrNo 23.75398503
## Q121011.fctrYes 0.00000000
## Q121699.fctrNo 0.00000000
## Q121699.fctrYes 17.89699784
## Q121700.fctrNo 0.00000000
## Q121700.fctrYes 0.00000000
## Q122120.fctrNo 0.00000000
## Q122120.fctrYes 0.00000000
## Q122769.fctrNo 0.00000000
## Q122769.fctrYes 0.00000000
## Q122770.fctrNo 0.00000000
## Q122770.fctrYes 0.00000000
## Q122771.fctrPc 17.06512114
## Q122771.fctrPt 28.00320961
## Q123464.fctrNo 0.00000000
## Q123464.fctrYes 0.00000000
## Q123621.fctrNo 0.00000000
## Q123621.fctrYes 25.62710047
## Q124122.fctrNo 0.00000000
## Q124122.fctrYes 18.64516818
## Q124742.fctrNo 0.00000000
## Q124742.fctrYes 0.00000000
## Q96024.fctrNo 0.00000000
## Q96024.fctrYes 0.00000000
## Q98078.fctrNo 0.00000000
## Q98078.fctrYes 0.00000000
## Q98578.fctrNo 0.00000000
## Q98578.fctrYes 0.00000000
## Q98869.fctrYes 0.00000000
## Q99480.fctrNo 0.00000000
## Q99581.fctrNo 0.00000000
## Q99581.fctrYes 0.00000000
## Q99716.fctrNo 0.00000000
## Q99716.fctrYes 0.00000000
## Q99982.fctrCheck! 0.00000000
## Q99982.fctrNope 0.00000000
## YOB.Age.fctr.C 0.00000000
## YOB.Age.fctr.Q 0.00000000
## YOB.Age.fctr^4 0.00000000
## YOB.Age.fctr^5 0.00000000
## YOB.Age.fctr^7 9.40830535
## YOB.Age.fctr^8 0.00000000
## Q109244.fctrNA:.clusterid.fctr2 0.00000000
## Q109244.fctrNA:.clusterid.fctr3 0.00000000
## Q109244.fctrNo:.clusterid.fctr2 0.00000000
## Q109244.fctrNo:.clusterid.fctr3 0.00000000
## Q109244.fctrNo:Edn.fctr.C 0.00000000
## Q109244.fctrNo:Edn.fctr.L 30.88881371
## Q109244.fctrNo:Edn.fctr.Q 0.00000000
## Q109244.fctrNo:Edn.fctr^4 0.00000000
## Q109244.fctrNo:Edn.fctr^5 0.00000000
## Q109244.fctrNo:Edn.fctr^6 0.00000000
## Q109244.fctrNo:Edn.fctr^7 0.00000000
## Q109244.fctrNo:Hhold.fctrMKn 0.00000000
## Q109244.fctrNo:Hhold.fctrMKy 0.00000000
## Q109244.fctrNo:Hhold.fctrPKn 0.00000000
## Q109244.fctrNo:Hhold.fctrPKy 0.00000000
## Q109244.fctrNo:Hhold.fctrSKn 0.00000000
## Q109244.fctrNo:Hhold.fctrSKy 0.00000000
## Q109244.fctrNo:Income.fctr.C 32.06273684
## Q109244.fctrNo:Income.fctr.L 43.29279743
## Q109244.fctrNo:Income.fctr.Q 0.00000000
## Q109244.fctrNo:Income.fctr^4 0.00000000
## Q109244.fctrNo:Income.fctr^5 0.00000000
## Q109244.fctrNo:Income.fctr^6 0.00000000
## Q109244.fctrNo:Q100689.fctrNo 0.00000000
## Q109244.fctrNo:Q100689.fctrYes 0.00000000
## Q109244.fctrNo:Q101163.fctrDad 0.00000000
## Q109244.fctrNo:Q101163.fctrMom 0.00000000
## Q109244.fctrNo:Q102674.fctrNo 0.00000000
## Q109244.fctrNo:Q102674.fctrYes 0.00000000
## Q109244.fctrNo:Q104996.fctrNo 0.00000000
## Q109244.fctrNo:Q104996.fctrYes 0.00000000
## Q109244.fctrNo:Q106997.fctrGr 0.00000000
## Q109244.fctrNo:Q106997.fctrYy 0.00000000
## Q109244.fctrNo:Q108342.fctrIn-person 0.00000000
## Q109244.fctrNo:Q108342.fctrOnline 0.00000000
## Q109244.fctrNo:Q108343.fctrNo 0.00000000
## Q109244.fctrNo:Q108343.fctrYes 0.00000000
## Q109244.fctrNo:Q113181.fctrNo 0.00000000
## Q109244.fctrNo:Q113181.fctrYes 64.54013369
## Q109244.fctrNo:Q115611.fctrNo 0.00000000
## Q109244.fctrNo:Q115611.fctrYes 0.00000000
## Q109244.fctrNo:Q116881.fctrHappy 0.00000000
## Q109244.fctrNo:Q116881.fctrRight 0.00000000
## Q109244.fctrNo:Q119334.fctrNo 0.00000000
## Q109244.fctrNo:Q119334.fctrYes 20.02571954
## Q109244.fctrNo:Q119650.fctrGiving 48.42576135
## Q109244.fctrNo:Q119650.fctrReceiving 0.00000000
## Q109244.fctrNo:Q120379.fctrNo 0.00000000
## Q109244.fctrNo:Q120379.fctrYes 0.00000000
## Q109244.fctrNo:Q121699.fctrNo 0.00000000
## Q109244.fctrNo:Q121699.fctrYes 0.00000000
## Q109244.fctrNo:Q122771.fctrPc 0.00000000
## Q109244.fctrNo:Q122771.fctrPt 0.00000000
## Q109244.fctrNo:Q123621.fctrNo 0.00000000
## Q109244.fctrNo:Q123621.fctrYes 0.00000000
## Q109244.fctrNo:Q124122.fctrNo 0.00000000
## Q109244.fctrNo:Q124122.fctrYes 0.00000000
## Q109244.fctrNo:Q98197.fctrNo 0.00000000
## Q109244.fctrNo:Q98197.fctrYes 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.C 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.L 0.00000000
## Q109244.fctrNo:YOB.Age.fctr.Q 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^4 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^5 0.00000000
## Q109244.fctrNo:YOB.Age.fctr^6 5.90465515
## Q109244.fctrNo:YOB.Age.fctr^7 4.91402305
## Q109244.fctrNo:YOB.Age.fctr^8 0.00000000
## Q109244.fctrYes:.clusterid.fctr2 0.00000000
## Q109244.fctrYes:.clusterid.fctr3 2.56126079
## Q109244.fctrYes:Edn.fctr.C 0.00000000
## Q109244.fctrYes:Edn.fctr.L 1.78716549
## Q109244.fctrYes:Edn.fctr.Q 0.00000000
## Q109244.fctrYes:Edn.fctr^4 0.00000000
## Q109244.fctrYes:Edn.fctr^5 0.00000000
## Q109244.fctrYes:Edn.fctr^6 0.00000000
## Q109244.fctrYes:Edn.fctr^7 8.86574323
## Q109244.fctrYes:Hhold.fctrMKn 0.00000000
## Q109244.fctrYes:Hhold.fctrMKy 0.00000000
## Q109244.fctrYes:Hhold.fctrPKn 0.00000000
## Q109244.fctrYes:Hhold.fctrPKy 0.00000000
## Q109244.fctrYes:Hhold.fctrSKn 0.00000000
## Q109244.fctrYes:Hhold.fctrSKy 0.00000000
## Q109244.fctrYes:Income.fctr.C 0.00000000
## Q109244.fctrYes:Income.fctr.L 34.85072480
## Q109244.fctrYes:Income.fctr.Q 0.00000000
## Q109244.fctrYes:Income.fctr^4 0.00000000
## Q109244.fctrYes:Income.fctr^5 0.00000000
## Q109244.fctrYes:Income.fctr^6 0.00000000
## Q109244.fctrYes:Q100689.fctrNo 0.00000000
## Q109244.fctrYes:Q100689.fctrYes 0.00000000
## Q109244.fctrYes:Q101163.fctrDad 0.00000000
## Q109244.fctrYes:Q101163.fctrMom 21.27931275
## Q109244.fctrYes:Q102674.fctrNo 0.00000000
## Q109244.fctrYes:Q102674.fctrYes 0.00000000
## Q109244.fctrYes:Q104996.fctrNo 0.00000000
## Q109244.fctrYes:Q104996.fctrYes 0.00000000
## Q109244.fctrYes:Q106997.fctrGr 0.00000000
## Q109244.fctrYes:Q106997.fctrYy 0.00000000
## Q109244.fctrYes:Q108342.fctrIn-person 0.00000000
## Q109244.fctrYes:Q108342.fctrOnline 0.00000000
## Q109244.fctrYes:Q108343.fctrNo 0.00000000
## Q109244.fctrYes:Q108343.fctrYes 0.00000000
## Q109244.fctrYes:Q113181.fctrNo 0.00000000
## Q109244.fctrYes:Q113181.fctrYes 0.00000000
## Q109244.fctrYes:Q115611.fctrNo 3.25707597
## Q109244.fctrYes:Q115611.fctrYes 0.00000000
## Q109244.fctrYes:Q116881.fctrHappy 29.38897891
## Q109244.fctrYes:Q116881.fctrRight 0.00000000
## Q109244.fctrYes:Q119334.fctrNo 0.00000000
## Q109244.fctrYes:Q119334.fctrYes 0.00000000
## Q109244.fctrYes:Q119650.fctrGiving 0.00000000
## Q109244.fctrYes:Q119650.fctrReceiving 0.00000000
## Q109244.fctrYes:Q120379.fctrNo 0.00000000
## Q109244.fctrYes:Q120379.fctrYes 0.00000000
## Q109244.fctrYes:Q121699.fctrNo 0.00000000
## Q109244.fctrYes:Q121699.fctrYes 0.00000000
## Q109244.fctrYes:Q122771.fctrPc 0.00000000
## Q109244.fctrYes:Q122771.fctrPt 0.00000000
## Q109244.fctrYes:Q123621.fctrNo 0.00000000
## Q109244.fctrYes:Q123621.fctrYes 25.21567205
## Q109244.fctrYes:Q124122.fctrNo 0.00000000
## Q109244.fctrYes:Q124122.fctrYes 0.00000000
## Q109244.fctrYes:Q98197.fctrNo 0.00000000
## Q109244.fctrYes:Q98197.fctrYes 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.C 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.L 0.00000000
## Q109244.fctrYes:YOB.Age.fctr.Q 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^4 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^5 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^6 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^7 0.00000000
## Q109244.fctrYes:YOB.Age.fctr^8 0.00000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 3.89746513
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.57949576
## YOB.Age.fctr(40,50]:YOB.Age.dff 10.93839173
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000000
## Final.RFE.X.Inc..rcv.bagEarth.imp
## Q109244.fctrYes 100.0000000
## Q115611.fctrYes 68.7216582
## Q109244.fctrNo 57.5096499
## Q98197.fctrNo 53.7611221
## Q118232.fctrId 49.3587931
## Q116881.fctrRight 45.4824539
## Hhold.fctrPKn 42.6897655
## Q101163.fctrMom 40.4164126
## Income.fctr.L 38.4555252
## Q108855.fctrYes! 36.2862315
## Q120379.fctrYes 34.7754057
## Q120472.fctrScience 32.6953240
## Q115390.fctrYes 31.0510047
## Q99480.fctrYes 28.7280604
## Q116953.fctrNo 28.2322850
## Q106993.fctrYes 25.8834449
## Hhold.fctrSKn 24.5014761
## YOB.Age.dff 22.8047037
## YOB.Age.fctr.L 21.3839541
## Q98869.fctrNo 21.0923871
## Q111220.fctrNo 18.5780239
## Edn.fctr^7 16.1217826
## Q105655.fctrYes 14.3154169
## YOB.Age.fctr^6 12.1056189
## Q102674.fctrYes 11.3446714
## Q98197.fctrYes 8.1852585
## Q115899.fctrMe 6.5397184
## .clusterid.fctr2 4.7815133
## .clusterid.fctr3 3.4657104
## Edn.fctr.L 1.9771493
## Edn.fctr.Q 1.3384782
## Edn.fctr.C 0.8520173
## Edn.fctr^4 0.4350240
## Edn.fctr^5 0.1217267
## Edn.fctr^6 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrMKy 0.0000000
## Hhold.fctrPKy 0.0000000
## Hhold.fctrSKy 0.0000000
## Income.fctr.C 0.0000000
## Income.fctr.Q 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Income.fctr^6 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrNo 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q100689.fctrYes 0.0000000
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101163.fctrDad 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrOwn 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 0.0000000
## Q105655.fctrNo 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106272.fctrNo 0.0000000
## Q106272.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrNo 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106997.fctrGr 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108855.fctrUmm... 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q109367.fctrNo 0.0000000
## Q109367.fctrYes 0.0000000
## Q110740.fctrMac 0.0000000
## Q110740.fctrPC 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 0.0000000
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113181.fctrNo 0.0000000
## Q113181.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrMysterious 0.0000000
## Q114386.fctrTMI 0.0000000
## Q114517.fctrNo 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrNo 0.0000000
## Q115195.fctrYes 0.0000000
## Q115390.fctrNo 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115611.fctrNo 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrCs 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116441.fctrNo 0.0000000
## Q116441.fctrYes 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrNo 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116881.fctrHappy 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCoolheaded 0.0000000
## Q117186.fctrHotheaded 0.0000000
## Q117193.fctrOddhours 0.0000000
## Q117193.fctrStandardhours 0.0000000
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrPr 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrNo 0.0000000
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrGiving 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrNo 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudyfirst 0.0000000
## Q120194.fctrTryfirst 0.0000000
## Q120379.fctrNo 0.0000000
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrNo 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q122771.fctrPt 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrNo 0.0000000
## Q96024.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99480.fctrNo 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99716.fctrYes 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000
## Q109244.fctrNA:.clusterid.fctr2 NA
## Q109244.fctrNA:.clusterid.fctr3 NA
## Q109244.fctrNo:.clusterid.fctr2 NA
## Q109244.fctrNo:.clusterid.fctr3 NA
## Q109244.fctrNo:Edn.fctr.C NA
## Q109244.fctrNo:Edn.fctr.L NA
## Q109244.fctrNo:Edn.fctr.Q NA
## Q109244.fctrNo:Edn.fctr^4 NA
## Q109244.fctrNo:Edn.fctr^5 NA
## Q109244.fctrNo:Edn.fctr^6 NA
## Q109244.fctrNo:Edn.fctr^7 NA
## Q109244.fctrNo:Hhold.fctrMKn NA
## Q109244.fctrNo:Hhold.fctrMKy NA
## Q109244.fctrNo:Hhold.fctrPKn NA
## Q109244.fctrNo:Hhold.fctrPKy NA
## Q109244.fctrNo:Hhold.fctrSKn NA
## Q109244.fctrNo:Hhold.fctrSKy NA
## Q109244.fctrNo:Income.fctr.C NA
## Q109244.fctrNo:Income.fctr.L NA
## Q109244.fctrNo:Income.fctr.Q NA
## Q109244.fctrNo:Income.fctr^4 NA
## Q109244.fctrNo:Income.fctr^5 NA
## Q109244.fctrNo:Income.fctr^6 NA
## Q109244.fctrNo:Q100689.fctrNo NA
## Q109244.fctrNo:Q100689.fctrYes NA
## Q109244.fctrNo:Q101163.fctrDad NA
## Q109244.fctrNo:Q101163.fctrMom NA
## Q109244.fctrNo:Q102674.fctrNo NA
## Q109244.fctrNo:Q102674.fctrYes NA
## Q109244.fctrNo:Q104996.fctrNo NA
## Q109244.fctrNo:Q104996.fctrYes NA
## Q109244.fctrNo:Q106997.fctrGr NA
## Q109244.fctrNo:Q106997.fctrYy NA
## Q109244.fctrNo:Q108342.fctrIn-person NA
## Q109244.fctrNo:Q108342.fctrOnline NA
## Q109244.fctrNo:Q108343.fctrNo NA
## Q109244.fctrNo:Q108343.fctrYes NA
## Q109244.fctrNo:Q113181.fctrNo NA
## Q109244.fctrNo:Q113181.fctrYes NA
## Q109244.fctrNo:Q115611.fctrNo NA
## Q109244.fctrNo:Q115611.fctrYes NA
## Q109244.fctrNo:Q116881.fctrHappy NA
## Q109244.fctrNo:Q116881.fctrRight NA
## Q109244.fctrNo:Q119334.fctrNo NA
## Q109244.fctrNo:Q119334.fctrYes NA
## Q109244.fctrNo:Q119650.fctrGiving NA
## Q109244.fctrNo:Q119650.fctrReceiving NA
## Q109244.fctrNo:Q120379.fctrNo NA
## Q109244.fctrNo:Q120379.fctrYes NA
## Q109244.fctrNo:Q121699.fctrNo NA
## Q109244.fctrNo:Q121699.fctrYes NA
## Q109244.fctrNo:Q122771.fctrPc NA
## Q109244.fctrNo:Q122771.fctrPt NA
## Q109244.fctrNo:Q123621.fctrNo NA
## Q109244.fctrNo:Q123621.fctrYes NA
## Q109244.fctrNo:Q124122.fctrNo NA
## Q109244.fctrNo:Q124122.fctrYes NA
## Q109244.fctrNo:Q98197.fctrNo NA
## Q109244.fctrNo:Q98197.fctrYes NA
## Q109244.fctrNo:YOB.Age.fctr.C NA
## Q109244.fctrNo:YOB.Age.fctr.L NA
## Q109244.fctrNo:YOB.Age.fctr.Q NA
## Q109244.fctrNo:YOB.Age.fctr^4 NA
## Q109244.fctrNo:YOB.Age.fctr^5 NA
## Q109244.fctrNo:YOB.Age.fctr^6 NA
## Q109244.fctrNo:YOB.Age.fctr^7 NA
## Q109244.fctrNo:YOB.Age.fctr^8 NA
## Q109244.fctrYes:.clusterid.fctr2 NA
## Q109244.fctrYes:.clusterid.fctr3 NA
## Q109244.fctrYes:Edn.fctr.C NA
## Q109244.fctrYes:Edn.fctr.L NA
## Q109244.fctrYes:Edn.fctr.Q NA
## Q109244.fctrYes:Edn.fctr^4 NA
## Q109244.fctrYes:Edn.fctr^5 NA
## Q109244.fctrYes:Edn.fctr^6 NA
## Q109244.fctrYes:Edn.fctr^7 NA
## Q109244.fctrYes:Hhold.fctrMKn NA
## Q109244.fctrYes:Hhold.fctrMKy NA
## Q109244.fctrYes:Hhold.fctrPKn NA
## Q109244.fctrYes:Hhold.fctrPKy NA
## Q109244.fctrYes:Hhold.fctrSKn NA
## Q109244.fctrYes:Hhold.fctrSKy NA
## Q109244.fctrYes:Income.fctr.C NA
## Q109244.fctrYes:Income.fctr.L NA
## Q109244.fctrYes:Income.fctr.Q NA
## Q109244.fctrYes:Income.fctr^4 NA
## Q109244.fctrYes:Income.fctr^5 NA
## Q109244.fctrYes:Income.fctr^6 NA
## Q109244.fctrYes:Q100689.fctrNo NA
## Q109244.fctrYes:Q100689.fctrYes NA
## Q109244.fctrYes:Q101163.fctrDad NA
## Q109244.fctrYes:Q101163.fctrMom NA
## Q109244.fctrYes:Q102674.fctrNo NA
## Q109244.fctrYes:Q102674.fctrYes NA
## Q109244.fctrYes:Q104996.fctrNo NA
## Q109244.fctrYes:Q104996.fctrYes NA
## Q109244.fctrYes:Q106997.fctrGr NA
## Q109244.fctrYes:Q106997.fctrYy NA
## Q109244.fctrYes:Q108342.fctrIn-person NA
## Q109244.fctrYes:Q108342.fctrOnline NA
## Q109244.fctrYes:Q108343.fctrNo NA
## Q109244.fctrYes:Q108343.fctrYes NA
## Q109244.fctrYes:Q113181.fctrNo NA
## Q109244.fctrYes:Q113181.fctrYes NA
## Q109244.fctrYes:Q115611.fctrNo NA
## Q109244.fctrYes:Q115611.fctrYes NA
## Q109244.fctrYes:Q116881.fctrHappy NA
## Q109244.fctrYes:Q116881.fctrRight NA
## Q109244.fctrYes:Q119334.fctrNo NA
## Q109244.fctrYes:Q119334.fctrYes NA
## Q109244.fctrYes:Q119650.fctrGiving NA
## Q109244.fctrYes:Q119650.fctrReceiving NA
## Q109244.fctrYes:Q120379.fctrNo NA
## Q109244.fctrYes:Q120379.fctrYes NA
## Q109244.fctrYes:Q121699.fctrNo NA
## Q109244.fctrYes:Q121699.fctrYes NA
## Q109244.fctrYes:Q122771.fctrPc NA
## Q109244.fctrYes:Q122771.fctrPt NA
## Q109244.fctrYes:Q123621.fctrNo NA
## Q109244.fctrYes:Q123621.fctrYes NA
## Q109244.fctrYes:Q124122.fctrNo NA
## Q109244.fctrYes:Q124122.fctrYes NA
## Q109244.fctrYes:Q98197.fctrNo NA
## Q109244.fctrYes:Q98197.fctrYes NA
## Q109244.fctrYes:YOB.Age.fctr.C NA
## Q109244.fctrYes:YOB.Age.fctr.L NA
## Q109244.fctrYes:YOB.Age.fctr.Q NA
## Q109244.fctrYes:YOB.Age.fctr^4 NA
## Q109244.fctrYes:YOB.Age.fctr^5 NA
## Q109244.fctrYes:YOB.Age.fctr^6 NA
## Q109244.fctrYes:YOB.Age.fctr^7 NA
## Q109244.fctrYes:YOB.Age.fctr^8 NA
## YOB.Age.fctr(15,20]:YOB.Age.dff NA
## YOB.Age.fctr(20,25]:YOB.Age.dff NA
## YOB.Age.fctr(25,30]:YOB.Age.dff NA
## YOB.Age.fctr(30,35]:YOB.Age.dff NA
## YOB.Age.fctr(35,40]:YOB.Age.dff NA
## YOB.Age.fctr(40,50]:YOB.Age.dff NA
## YOB.Age.fctr(50,65]:YOB.Age.dff NA
## YOB.Age.fctr(65,90]:YOB.Age.dff NA
## YOB.Age.fctrNA:YOB.Age.dff NA
## imp
## Q109244.fctrYes 100.0000000
## Q115611.fctrYes 68.7216582
## Q109244.fctrNo 57.5096499
## Q98197.fctrNo 53.7611221
## Q118232.fctrId 49.3587931
## Q116881.fctrRight 45.4824539
## Hhold.fctrPKn 42.6897655
## Q101163.fctrMom 40.4164126
## Income.fctr.L 38.4555252
## Q108855.fctrYes! 36.2862315
## Q120379.fctrYes 34.7754057
## Q120472.fctrScience 32.6953240
## Q115390.fctrYes 31.0510047
## Q99480.fctrYes 28.7280604
## Q116953.fctrNo 28.2322850
## Q106993.fctrYes 25.8834449
## Hhold.fctrSKn 24.5014761
## YOB.Age.dff 22.8047037
## YOB.Age.fctr.L 21.3839541
## Q98869.fctrNo 21.0923871
## Q111220.fctrNo 18.5780239
## Edn.fctr^7 16.1217826
## Q105655.fctrYes 14.3154169
## YOB.Age.fctr^6 12.1056189
## Q102674.fctrYes 11.3446714
## Q98197.fctrYes 8.1852585
## Q115899.fctrMe 6.5397184
## .clusterid.fctr2 4.7815133
## .clusterid.fctr3 3.4657104
## Edn.fctr.L 1.9771493
## Edn.fctr.Q 1.3384782
## Edn.fctr.C 0.8520173
## Edn.fctr^4 0.4350240
## Edn.fctr^5 0.1217267
## Edn.fctr^6 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrMKy 0.0000000
## Hhold.fctrPKy 0.0000000
## Hhold.fctrSKy 0.0000000
## Income.fctr.C 0.0000000
## Income.fctr.Q 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Income.fctr^6 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrNo 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q100689.fctrYes 0.0000000
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101163.fctrDad 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrOwn 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 0.0000000
## Q105655.fctrNo 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106272.fctrNo 0.0000000
## Q106272.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrNo 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106997.fctrGr 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108855.fctrUmm... 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q109367.fctrNo 0.0000000
## Q109367.fctrYes 0.0000000
## Q110740.fctrMac 0.0000000
## Q110740.fctrPC 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 0.0000000
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113181.fctrNo 0.0000000
## Q113181.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrMysterious 0.0000000
## Q114386.fctrTMI 0.0000000
## Q114517.fctrNo 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrNo 0.0000000
## Q115195.fctrYes 0.0000000
## Q115390.fctrNo 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115611.fctrNo 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrCs 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116441.fctrNo 0.0000000
## Q116441.fctrYes 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrNo 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116881.fctrHappy 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCoolheaded 0.0000000
## Q117186.fctrHotheaded 0.0000000
## Q117193.fctrOddhours 0.0000000
## Q117193.fctrStandardhours 0.0000000
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrPr 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrNo 0.0000000
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrGiving 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrNo 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudyfirst 0.0000000
## Q120194.fctrTryfirst 0.0000000
## Q120379.fctrNo 0.0000000
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrNo 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q122771.fctrPt 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrNo 0.0000000
## Q96024.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99480.fctrNo 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99716.fctrYes 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000
## Q109244.fctrNA:.clusterid.fctr2 NA
## Q109244.fctrNA:.clusterid.fctr3 NA
## Q109244.fctrNo:.clusterid.fctr2 NA
## Q109244.fctrNo:.clusterid.fctr3 NA
## Q109244.fctrNo:Edn.fctr.C NA
## Q109244.fctrNo:Edn.fctr.L NA
## Q109244.fctrNo:Edn.fctr.Q NA
## Q109244.fctrNo:Edn.fctr^4 NA
## Q109244.fctrNo:Edn.fctr^5 NA
## Q109244.fctrNo:Edn.fctr^6 NA
## Q109244.fctrNo:Edn.fctr^7 NA
## Q109244.fctrNo:Hhold.fctrMKn NA
## Q109244.fctrNo:Hhold.fctrMKy NA
## Q109244.fctrNo:Hhold.fctrPKn NA
## Q109244.fctrNo:Hhold.fctrPKy NA
## Q109244.fctrNo:Hhold.fctrSKn NA
## Q109244.fctrNo:Hhold.fctrSKy NA
## Q109244.fctrNo:Income.fctr.C NA
## Q109244.fctrNo:Income.fctr.L NA
## Q109244.fctrNo:Income.fctr.Q NA
## Q109244.fctrNo:Income.fctr^4 NA
## Q109244.fctrNo:Income.fctr^5 NA
## Q109244.fctrNo:Income.fctr^6 NA
## Q109244.fctrNo:Q100689.fctrNo NA
## Q109244.fctrNo:Q100689.fctrYes NA
## Q109244.fctrNo:Q101163.fctrDad NA
## Q109244.fctrNo:Q101163.fctrMom NA
## Q109244.fctrNo:Q102674.fctrNo NA
## Q109244.fctrNo:Q102674.fctrYes NA
## Q109244.fctrNo:Q104996.fctrNo NA
## Q109244.fctrNo:Q104996.fctrYes NA
## Q109244.fctrNo:Q106997.fctrGr NA
## Q109244.fctrNo:Q106997.fctrYy NA
## Q109244.fctrNo:Q108342.fctrIn-person NA
## Q109244.fctrNo:Q108342.fctrOnline NA
## Q109244.fctrNo:Q108343.fctrNo NA
## Q109244.fctrNo:Q108343.fctrYes NA
## Q109244.fctrNo:Q113181.fctrNo NA
## Q109244.fctrNo:Q113181.fctrYes NA
## Q109244.fctrNo:Q115611.fctrNo NA
## Q109244.fctrNo:Q115611.fctrYes NA
## Q109244.fctrNo:Q116881.fctrHappy NA
## Q109244.fctrNo:Q116881.fctrRight NA
## Q109244.fctrNo:Q119334.fctrNo NA
## Q109244.fctrNo:Q119334.fctrYes NA
## Q109244.fctrNo:Q119650.fctrGiving NA
## Q109244.fctrNo:Q119650.fctrReceiving NA
## Q109244.fctrNo:Q120379.fctrNo NA
## Q109244.fctrNo:Q120379.fctrYes NA
## Q109244.fctrNo:Q121699.fctrNo NA
## Q109244.fctrNo:Q121699.fctrYes NA
## Q109244.fctrNo:Q122771.fctrPc NA
## Q109244.fctrNo:Q122771.fctrPt NA
## Q109244.fctrNo:Q123621.fctrNo NA
## Q109244.fctrNo:Q123621.fctrYes NA
## Q109244.fctrNo:Q124122.fctrNo NA
## Q109244.fctrNo:Q124122.fctrYes NA
## Q109244.fctrNo:Q98197.fctrNo NA
## Q109244.fctrNo:Q98197.fctrYes NA
## Q109244.fctrNo:YOB.Age.fctr.C NA
## Q109244.fctrNo:YOB.Age.fctr.L NA
## Q109244.fctrNo:YOB.Age.fctr.Q NA
## Q109244.fctrNo:YOB.Age.fctr^4 NA
## Q109244.fctrNo:YOB.Age.fctr^5 NA
## Q109244.fctrNo:YOB.Age.fctr^6 NA
## Q109244.fctrNo:YOB.Age.fctr^7 NA
## Q109244.fctrNo:YOB.Age.fctr^8 NA
## Q109244.fctrYes:.clusterid.fctr2 NA
## Q109244.fctrYes:.clusterid.fctr3 NA
## Q109244.fctrYes:Edn.fctr.C NA
## Q109244.fctrYes:Edn.fctr.L NA
## Q109244.fctrYes:Edn.fctr.Q NA
## Q109244.fctrYes:Edn.fctr^4 NA
## Q109244.fctrYes:Edn.fctr^5 NA
## Q109244.fctrYes:Edn.fctr^6 NA
## Q109244.fctrYes:Edn.fctr^7 NA
## Q109244.fctrYes:Hhold.fctrMKn NA
## Q109244.fctrYes:Hhold.fctrMKy NA
## Q109244.fctrYes:Hhold.fctrPKn NA
## Q109244.fctrYes:Hhold.fctrPKy NA
## Q109244.fctrYes:Hhold.fctrSKn NA
## Q109244.fctrYes:Hhold.fctrSKy NA
## Q109244.fctrYes:Income.fctr.C NA
## Q109244.fctrYes:Income.fctr.L NA
## Q109244.fctrYes:Income.fctr.Q NA
## Q109244.fctrYes:Income.fctr^4 NA
## Q109244.fctrYes:Income.fctr^5 NA
## Q109244.fctrYes:Income.fctr^6 NA
## Q109244.fctrYes:Q100689.fctrNo NA
## Q109244.fctrYes:Q100689.fctrYes NA
## Q109244.fctrYes:Q101163.fctrDad NA
## Q109244.fctrYes:Q101163.fctrMom NA
## Q109244.fctrYes:Q102674.fctrNo NA
## Q109244.fctrYes:Q102674.fctrYes NA
## Q109244.fctrYes:Q104996.fctrNo NA
## Q109244.fctrYes:Q104996.fctrYes NA
## Q109244.fctrYes:Q106997.fctrGr NA
## Q109244.fctrYes:Q106997.fctrYy NA
## Q109244.fctrYes:Q108342.fctrIn-person NA
## Q109244.fctrYes:Q108342.fctrOnline NA
## Q109244.fctrYes:Q108343.fctrNo NA
## Q109244.fctrYes:Q108343.fctrYes NA
## Q109244.fctrYes:Q113181.fctrNo NA
## Q109244.fctrYes:Q113181.fctrYes NA
## Q109244.fctrYes:Q115611.fctrNo NA
## Q109244.fctrYes:Q115611.fctrYes NA
## Q109244.fctrYes:Q116881.fctrHappy NA
## Q109244.fctrYes:Q116881.fctrRight NA
## Q109244.fctrYes:Q119334.fctrNo NA
## Q109244.fctrYes:Q119334.fctrYes NA
## Q109244.fctrYes:Q119650.fctrGiving NA
## Q109244.fctrYes:Q119650.fctrReceiving NA
## Q109244.fctrYes:Q120379.fctrNo NA
## Q109244.fctrYes:Q120379.fctrYes NA
## Q109244.fctrYes:Q121699.fctrNo NA
## Q109244.fctrYes:Q121699.fctrYes NA
## Q109244.fctrYes:Q122771.fctrPc NA
## Q109244.fctrYes:Q122771.fctrPt NA
## Q109244.fctrYes:Q123621.fctrNo NA
## Q109244.fctrYes:Q123621.fctrYes NA
## Q109244.fctrYes:Q124122.fctrNo NA
## Q109244.fctrYes:Q124122.fctrYes NA
## Q109244.fctrYes:Q98197.fctrNo NA
## Q109244.fctrYes:Q98197.fctrYes NA
## Q109244.fctrYes:YOB.Age.fctr.C NA
## Q109244.fctrYes:YOB.Age.fctr.L NA
## Q109244.fctrYes:YOB.Age.fctr.Q NA
## Q109244.fctrYes:YOB.Age.fctr^4 NA
## Q109244.fctrYes:YOB.Age.fctr^5 NA
## Q109244.fctrYes:YOB.Age.fctr^6 NA
## Q109244.fctrYes:YOB.Age.fctr^7 NA
## Q109244.fctrYes:YOB.Age.fctr^8 NA
## YOB.Age.fctr(15,20]:YOB.Age.dff NA
## YOB.Age.fctr(20,25]:YOB.Age.dff NA
## YOB.Age.fctr(25,30]:YOB.Age.dff NA
## YOB.Age.fctr(30,35]:YOB.Age.dff NA
## YOB.Age.fctr(35,40]:YOB.Age.dff NA
## YOB.Age.fctr(40,50]:YOB.Age.dff NA
## YOB.Age.fctr(50,65]:YOB.Age.dff NA
## YOB.Age.fctr(65,90]:YOB.Age.dff NA
## YOB.Age.fctrNA:YOB.Age.dff NAif (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 107
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.RFE.X.Inc..rcv.bagEarth.prob
## 1 626 R 0.06310623
## 2 1236 R NA
## 3 3895 R NA
## 4 468 R NA
## 5 1307 R NA
## 6 1515 R NA
## Party.fctr.RFE.X.Inc..rcv.bagEarth
## 1 D
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err
## 1 TRUE
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err.abs
## 1 0.9368938
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## Party.fctr.RFE.X.Inc..rcv.bagEarth.is.acc
## 1 FALSE
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.prob
## 1 0.05910248
## 2 0.06824773
## 3 0.07025073
## 4 0.07437708
## 5 0.07918594
## 6 0.08104531
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth
## 1 D
## 2 D
## 3 D
## 4 D
## 5 D
## 6 D
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err.abs
## 1 0.9408975
## 2 0.9317523
## 3 0.9297493
## 4 0.9256229
## 5 0.9208141
## 6 0.9189547
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.error
## 1 -0.5408975
## 2 -0.5317523
## 3 -0.5297493
## 4 -0.5256229
## 5 -0.5208141
## 6 -0.5189547
## USER_ID Party.fctr Party.fctr.RFE.X.Inc..rcv.bagEarth.prob
## 213 689 R 0.3517267
## 259 4528 R NA
## 851 6746 R NA
## 1021 4819 R 0.5504459
## 1291 4848 R 0.4090260
## 1686 1551 R 0.5974762
## Party.fctr.RFE.X.Inc..rcv.bagEarth
## 213 D
## 259 <NA>
## 851 <NA>
## 1021 D
## 1291 D
## 1686 D
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err
## 213 TRUE
## 259 NA
## 851 NA
## 1021 TRUE
## 1291 TRUE
## 1686 TRUE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err.abs
## 213 0.6482733
## 259 NA
## 851 NA
## 1021 0.4495541
## 1291 0.5909740
## 1686 0.4025238
## Party.fctr.RFE.X.Inc..rcv.bagEarth.is.acc
## 213 FALSE
## 259 NA
## 851 NA
## 1021 FALSE
## 1291 FALSE
## 1686 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.prob
## 213 0.3349243
## 259 0.3601673
## 851 0.4755784
## 1021 0.4949405
## 1291 0.5301022
## 1686 0.5943975
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth
## 213 D
## 259 D
## 851 D
## 1021 D
## 1291 D
## 1686 D
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err
## 213 TRUE
## 259 TRUE
## 851 TRUE
## 1021 TRUE
## 1291 TRUE
## 1686 TRUE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err.abs
## 213 0.6650757
## 259 0.6398327
## 851 0.5244216
## 1021 0.5050595
## 1291 0.4698978
## 1686 0.4056025
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.is.acc
## 213 FALSE
## 259 FALSE
## 851 FALSE
## 1021 FALSE
## 1291 FALSE
## 1686 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.accurate
## 213 FALSE
## 259 FALSE
## 851 FALSE
## 1021 FALSE
## 1291 FALSE
## 1686 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.error
## 213 -0.265075652
## 259 -0.239832655
## 851 -0.124421595
## 1021 -0.105059549
## 1291 -0.069897843
## 1686 -0.005602456
## USER_ID Party.fctr Party.fctr.RFE.X.Inc..rcv.bagEarth.prob
## 2057 3433 D 0.8487389
## 2058 1393 D 0.8377839
## 2059 5889 D 0.8721910
## 2060 3006 D NA
## 2061 2641 D 0.8600793
## 2062 1311 D 0.8149355
## Party.fctr.RFE.X.Inc..rcv.bagEarth
## 2057 R
## 2058 R
## 2059 R
## 2060 <NA>
## 2061 R
## 2062 R
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err
## 2057 TRUE
## 2058 TRUE
## 2059 TRUE
## 2060 NA
## 2061 TRUE
## 2062 TRUE
## Party.fctr.RFE.X.Inc..rcv.bagEarth.err.abs
## 2057 0.8487389
## 2058 0.8377839
## 2059 0.8721910
## 2060 NA
## 2061 0.8600793
## 2062 0.8149355
## Party.fctr.RFE.X.Inc..rcv.bagEarth.is.acc
## 2057 FALSE
## 2058 FALSE
## 2059 FALSE
## 2060 NA
## 2061 FALSE
## 2062 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.prob
## 2057 0.8210583
## 2058 0.8252962
## 2059 0.8335500
## 2060 0.8382623
## 2061 0.8402027
## 2062 0.8456766
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth
## 2057 R
## 2058 R
## 2059 R
## 2060 R
## 2061 R
## 2062 R
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err
## 2057 TRUE
## 2058 TRUE
## 2059 TRUE
## 2060 TRUE
## 2061 TRUE
## 2062 TRUE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err.abs
## 2057 0.8210583
## 2058 0.8252962
## 2059 0.8335500
## 2060 0.8382623
## 2061 0.8402027
## 2062 0.8456766
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.is.acc
## 2057 FALSE
## 2058 FALSE
## 2059 FALSE
## 2060 FALSE
## 2061 FALSE
## 2062 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.accurate
## 2057 FALSE
## 2058 FALSE
## 2059 FALSE
## 2060 FALSE
## 2061 FALSE
## 2062 FALSE
## Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.error
## 2057 0.2210583
## 2058 0.2252962
## 2059 0.2335500
## 2060 0.2382623
## 2061 0.2402027
## 2062 0.2456766
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.prob"
## [2] "Party.fctr.Final.RFE.X.Inc..rcv.bagEarth"
## [3] "Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err"
## [4] "Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.err.abs"
## [5] "Party.fctr.Final.RFE.X.Inc..rcv.bagEarth.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 5 fit.data.training 2 1 1 759.834 804.753
## 6 predict.data.new 3 0 0 804.754 NA
## elapsed
## 5 44.92
## 6 NAStep 3.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.6
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.6## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 107## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] 0.6## [1] "glbMdlSelId: RFE.X.Inc##rcv#bagEarth"## [1] "glbMdlFinId: Final.RFE.X.Inc##rcv#bagEarth"## [1] "Cross Validation issues:"
## RFE.X##rcv#bagEarth RFE.X.Inc##rcv#bagEarth
## 1 1
## Final.RFE.X.Inc##rcv#bagEarth
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## RFE.X.Inc##rcv#bagEarth 0.5838565 0.5840728
## RFE.X##rcv#bagEarth 0.5838565 0.5829523
## RFE.X#zv.pca#rcv#glmnet 0.5829596 0.5816510
## All.X#nzv.spatialSign#rcv#glmnet 0.5820628 0.5787416
## All.X#spatialSign#rcv#glmnet 0.5811659 0.5793099
## RFE.X#nzv.spatialSign#rcv#glmnet 0.5811659 0.5785672
## All.X.Inc#nzv.spatialSign#rcv#glmnet 0.5811659 0.5734523
## RFE.X#spatialSign#rcv#glmnet 0.5802691 0.5794035
## All.X#nzv#rcv#glmnet 0.5775785 0.5766539
## RFE.X#nzv#rcv#glmnet 0.5775785 0.5762019
## All.X#expoTrans#rcv#glmnet 0.5775785 0.5745421
## RFE.X#expoTrans#rcv#glmnet 0.5775785 0.5745421
## RFE.X#YeoJohnson#rcv#glmnet 0.5775785 0.5744824
## All.X#YeoJohnson#rcv#glmnet 0.5775785 0.5744824
## RFE.X##rcv#rpart 0.5775785 0.5547284
## RFE.X##rcv#glmnet 0.5766816 0.5757966
## RFE.X#zv#rcv#glmnet 0.5766816 0.5757966
## RFE.X#BoxCox#rcv#glmnet 0.5766816 0.5757966
## RFE.X#center#rcv#glmnet 0.5766816 0.5757966
## RFE.X#scale#rcv#glmnet 0.5766816 0.5757966
## RFE.X#center.scale#rcv#glmnet 0.5766816 0.5757966
## RFE.X#range#rcv#glmnet 0.5766816 0.5757966
## RFE.X#conditionalX#rcv#glmnet 0.5766816 0.5757966
## Low.cor.X##rcv#glmnet 0.5766816 0.5757966
## All.X##rcv#glmnet 0.5766816 0.5757966
## All.X#zv#rcv#glmnet 0.5766816 0.5757966
## All.X#BoxCox#rcv#glmnet 0.5766816 0.5757966
## All.X#center#rcv#glmnet 0.5766816 0.5757966
## All.X#scale#rcv#glmnet 0.5766816 0.5757966
## All.X#center.scale#rcv#glmnet 0.5766816 0.5757966
## All.X#range#rcv#glmnet 0.5766816 0.5757966
## All.X#conditionalX#rcv#glmnet 0.5766816 0.5757966
## All.X#zv.pca#rcv#glmnet 0.5748879 0.5798104
## RFE.X##rcv#gbm 0.5730942 0.5783993
## RFE.X#nzv.pca.spatialSign#rcv#glmnet 0.5713004 0.5742305
## RFE.X##rcv#earth 0.5677130 0.5720864
## RFE.X##rcv#glm 0.5668161 0.5690413
## All.X##rcv#glm 0.5650224 0.5687249
## RFE.X##rcv#rf 0.5650224 0.5510859
## RFE.X##rcv#avNNet 0.5632287 0.5713598
## RFE.X##rcv#nnet 0.5623318 0.5667713
## RFE.X#ica#rcv#glmnet 0.5363229 0.5370894
## All.X#ica#rcv#glmnet 0.5354260 0.5360626
## Interact.High.cor.Y##rcv#glmnet 0.5345291 0.5242069
## Random###myrandom_classfr 0.5300448 0.5181895
## Max.cor.Y.rcv.1X1###glmnet 0.5300448 0.5102459
## Max.cor.Y##rcv#rpart 0.5300448 0.5000646
## MFO###myMFO_classfr 0.5300448 0.5000000
## Final.RFE.X.Inc##rcv#bagEarth NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## RFE.X.Inc##rcv#bagEarth 0.5671007 0.6169886
## RFE.X##rcv#bagEarth 0.5601161 0.6288811
## RFE.X#zv.pca#rcv#glmnet 0.5792308 0.6459313
## All.X#nzv.spatialSign#rcv#glmnet 0.5572148 0.6478785
## All.X#spatialSign#rcv#glmnet 0.5570372 0.6492270
## RFE.X#nzv.spatialSign#rcv#glmnet 0.5597528 0.6462317
## All.X.Inc#nzv.spatialSign#rcv#glmnet 0.5556503 0.6440612
## RFE.X#spatialSign#rcv#glmnet 0.5570372 0.6490772
## All.X#nzv#rcv#glmnet 0.5550900 0.6475806
## RFE.X#nzv#rcv#glmnet 0.5531816 0.6466826
## All.X#expoTrans#rcv#glmnet 0.5559554 0.6463076
## RFE.X#expoTrans#rcv#glmnet 0.5559554 0.6462327
## RFE.X#YeoJohnson#rcv#glmnet 0.5559554 0.6466820
## All.X#YeoJohnson#rcv#glmnet 0.5559554 0.6465321
## RFE.X##rcv#rpart 0.5677723 0.6207805
## RFE.X##rcv#glmnet 0.5588180 0.6475052
## RFE.X#zv#rcv#glmnet 0.5588180 0.6475052
## RFE.X#BoxCox#rcv#glmnet 0.5588180 0.6475052
## RFE.X#center#rcv#glmnet 0.5588180 0.6475052
## RFE.X#scale#rcv#glmnet 0.5588180 0.6475052
## RFE.X#center.scale#rcv#glmnet 0.5588180 0.6475052
## RFE.X#range#rcv#glmnet 0.5588180 0.6475052
## RFE.X#conditionalX#rcv#glmnet 0.5588180 0.6475052
## Low.cor.X##rcv#glmnet 0.5588180 0.6471308
## All.X##rcv#glmnet 0.5588180 0.6471308
## All.X#zv#rcv#glmnet 0.5588180 0.6471308
## All.X#BoxCox#rcv#glmnet 0.5588180 0.6471308
## All.X#center#rcv#glmnet 0.5588180 0.6471308
## All.X#scale#rcv#glmnet 0.5588180 0.6471308
## All.X#center.scale#rcv#glmnet 0.5588180 0.6471308
## All.X#range#rcv#glmnet 0.5588180 0.6471308
## All.X#conditionalX#rcv#glmnet 0.5588180 0.6471308
## All.X#zv.pca#rcv#glmnet 0.5717247 0.6421158
## RFE.X##rcv#gbm 0.5638748 0.6477310
## RFE.X#nzv.pca.spatialSign#rcv#glmnet 0.5545298 0.6480297
## RFE.X##rcv#earth 0.5641493 0.6400213
## RFE.X##rcv#glm 0.5497475 0.6255716
## All.X##rcv#glm 0.5487933 0.6254219
## RFE.X##rcv#rf 0.5382083 1.0000000
## RFE.X##rcv#avNNet 0.5593589 0.6367252
## RFE.X##rcv#nnet 0.5584628 0.6155403
## RFE.X#ica#rcv#glmnet 0.5121737 0.5603719
## All.X#ica#rcv#glmnet 0.5133442 0.5607462
## Interact.High.cor.Y##rcv#glmnet 0.5218093 0.6250526
## Random###myrandom_classfr 0.4836608 0.5299798
## Max.cor.Y.rcv.1X1###glmnet 0.4999322 0.6240737
## Max.cor.Y##rcv#rpart 0.4999322 0.6227308
## MFO###myMFO_classfr 0.5000000 0.5299798
## Final.RFE.X.Inc##rcv#bagEarth NA 0.6548132
## opt.prob.threshold.fit
## RFE.X.Inc##rcv#bagEarth 0.50
## RFE.X##rcv#bagEarth 0.50
## RFE.X#zv.pca#rcv#glmnet 0.50
## All.X#nzv.spatialSign#rcv#glmnet 0.50
## All.X#spatialSign#rcv#glmnet 0.50
## RFE.X#nzv.spatialSign#rcv#glmnet 0.50
## All.X.Inc#nzv.spatialSign#rcv#glmnet 0.50
## RFE.X#spatialSign#rcv#glmnet 0.50
## All.X#nzv#rcv#glmnet 0.50
## RFE.X#nzv#rcv#glmnet 0.50
## All.X#expoTrans#rcv#glmnet 0.50
## RFE.X#expoTrans#rcv#glmnet 0.50
## RFE.X#YeoJohnson#rcv#glmnet 0.50
## All.X#YeoJohnson#rcv#glmnet 0.50
## RFE.X##rcv#rpart 0.50
## RFE.X##rcv#glmnet 0.50
## RFE.X#zv#rcv#glmnet 0.50
## RFE.X#BoxCox#rcv#glmnet 0.50
## RFE.X#center#rcv#glmnet 0.50
## RFE.X#scale#rcv#glmnet 0.50
## RFE.X#center.scale#rcv#glmnet 0.50
## RFE.X#range#rcv#glmnet 0.50
## RFE.X#conditionalX#rcv#glmnet 0.50
## Low.cor.X##rcv#glmnet 0.50
## All.X##rcv#glmnet 0.50
## All.X#zv#rcv#glmnet 0.50
## All.X#BoxCox#rcv#glmnet 0.50
## All.X#center#rcv#glmnet 0.50
## All.X#scale#rcv#glmnet 0.50
## All.X#center.scale#rcv#glmnet 0.50
## All.X#range#rcv#glmnet 0.50
## All.X#conditionalX#rcv#glmnet 0.50
## All.X#zv.pca#rcv#glmnet 0.50
## RFE.X##rcv#gbm 0.50
## RFE.X#nzv.pca.spatialSign#rcv#glmnet 0.50
## RFE.X##rcv#earth 0.50
## RFE.X##rcv#glm 0.50
## All.X##rcv#glm 0.50
## RFE.X##rcv#rf 0.50
## RFE.X##rcv#avNNet 0.50
## RFE.X##rcv#nnet 0.50
## RFE.X#ica#rcv#glmnet 0.50
## All.X#ica#rcv#glmnet 0.50
## Interact.High.cor.Y##rcv#glmnet 0.50
## Random###myrandom_classfr 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.45
## Max.cor.Y##rcv#rpart 0.50
## MFO###myMFO_classfr 0.50
## Final.RFE.X.Inc##rcv#bagEarth 0.50
## opt.prob.threshold.OOB
## RFE.X.Inc##rcv#bagEarth 0.60
## RFE.X##rcv#bagEarth 0.60
## RFE.X#zv.pca#rcv#glmnet 0.50
## All.X#nzv.spatialSign#rcv#glmnet 0.60
## All.X#spatialSign#rcv#glmnet 0.60
## RFE.X#nzv.spatialSign#rcv#glmnet 0.60
## All.X.Inc#nzv.spatialSign#rcv#glmnet 0.60
## RFE.X#spatialSign#rcv#glmnet 0.60
## All.X#nzv#rcv#glmnet 0.60
## RFE.X#nzv#rcv#glmnet 0.55
## All.X#expoTrans#rcv#glmnet 0.55
## RFE.X#expoTrans#rcv#glmnet 0.55
## RFE.X#YeoJohnson#rcv#glmnet 0.55
## All.X#YeoJohnson#rcv#glmnet 0.55
## RFE.X##rcv#rpart 0.50
## RFE.X##rcv#glmnet 0.55
## RFE.X#zv#rcv#glmnet 0.55
## RFE.X#BoxCox#rcv#glmnet 0.55
## RFE.X#center#rcv#glmnet 0.55
## RFE.X#scale#rcv#glmnet 0.55
## RFE.X#center.scale#rcv#glmnet 0.55
## RFE.X#range#rcv#glmnet 0.55
## RFE.X#conditionalX#rcv#glmnet 0.55
## Low.cor.X##rcv#glmnet 0.55
## All.X##rcv#glmnet 0.55
## All.X#zv#rcv#glmnet 0.55
## All.X#BoxCox#rcv#glmnet 0.55
## All.X#center#rcv#glmnet 0.55
## All.X#scale#rcv#glmnet 0.55
## All.X#center.scale#rcv#glmnet 0.55
## All.X#range#rcv#glmnet 0.55
## All.X#conditionalX#rcv#glmnet 0.55
## All.X#zv.pca#rcv#glmnet 0.50
## RFE.X##rcv#gbm 0.60
## RFE.X#nzv.pca.spatialSign#rcv#glmnet 0.55
## RFE.X##rcv#earth 0.60
## RFE.X##rcv#glm 0.65
## All.X##rcv#glm 0.65
## RFE.X##rcv#rf 0.65
## RFE.X##rcv#avNNet 0.65
## RFE.X##rcv#nnet 0.65
## RFE.X#ica#rcv#glmnet 0.55
## All.X#ica#rcv#glmnet 0.55
## Interact.High.cor.Y##rcv#glmnet 0.70
## Random###myrandom_classfr 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.65
## Max.cor.Y##rcv#rpart 0.65
## MFO###myMFO_classfr 0.50
## Final.RFE.X.Inc##rcv#bagEarth NA## [1] "RFE.X.Inc##rcv#bagEarth OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 483 108
## R 356 168## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## NA 814.9300 208.8033 1031.1905 NA
## No 855.6562 236.6868 1109.6961 NA
## Yes 161.9998 84.8316 280.0988 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## NA 0.3920952 0.3928251 0.3929598 1746 533 14
## No 0.4403773 0.4466368 0.4468391 1961 343 279
## Yes 0.1675275 0.1605381 0.1602011 746 223 NA
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## NA 438 1171 1013 547 1746 547 2184 0.4767199
## No 498 1038 1421 622 1961 622 2459 0.4752746
## Yes 179 742 183 223 746 223 925 0.4739195
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## NA 0.4667411 NA 0.4721568
## No 0.4363366 NA 0.4512794
## Yes 0.2171579 NA 0.3028095
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 1832.585921 530.321671 2420.985436 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 4453.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 1099.000000 NA 1115.000000 2951.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 2617.000000 1392.000000 4453.000000 1392.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 5568.000000 1.425914 1.120236 NA
## err.abs.trn.mean
## 1.226246## [1] "Features Importance for selected models:"## RFE.X.Inc..rcv.bagEarth.imp
## Q109244.fctrYes 100.000000
## Q109244.fctrNo:Q113181.fctrYes 64.540134
## Q115611.fctrYes 55.612062
## Q98197.fctrNo 52.696910
## Q109244.fctrNo:Q119650.fctrGiving 48.425761
## Q116881.fctrHappy 46.091920
## Q109244.fctrNo:Income.fctr.L 43.292797
## Hhold.fctrPKn 41.178878
## Q106997.fctrGr 39.525232
## Q108342.fctrOnline 37.784846
## Edn.fctr^7 36.299408
## Q109244.fctrYes:Income.fctr.L 34.850725
## Q120379.fctrNo 33.742494
## Q109244.fctrNo:Income.fctr.C 32.062737
## Q109244.fctrNo:Edn.fctr.L 30.888814
## Q109244.fctrYes:Q116881.fctrHappy 29.388979
## Q122771.fctrPt 28.003210
## Q100689.fctrYes 26.868983
## Q123621.fctrYes 25.627100
## Q109244.fctrYes:Q123621.fctrYes 25.215672
## Q121011.fctrNo 23.753985
## Q101163.fctrDad 22.292071
## Q109244.fctrYes:Q101163.fctrMom 21.279313
## Q109244.fctrNo:Q119334.fctrYes 20.025720
## Q124122.fctrYes 18.645168
## Q121699.fctrYes 17.896998
## Q122771.fctrPc 17.065121
## Q104996.fctrYes 15.363847
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.579496
## Q108343.fctrYes 13.211562
## Q102674.fctrYes 12.081122
## YOB.Age.fctr(40,50]:YOB.Age.dff 10.938392
## YOB.Age.fctr.L 6.573204
## Q109244.fctrNo 0.000000
## Q118232.fctrId 0.000000
## Q116881.fctrRight 0.000000
## Q101163.fctrMom 0.000000
## Income.fctr.L 0.000000
## Q108855.fctrYes! 0.000000
## Q120379.fctrYes 0.000000
## Q120472.fctrScience 0.000000
## Q115390.fctrYes 0.000000
## Q99480.fctrYes 0.000000
## Q116953.fctrNo 0.000000
## Q106993.fctrYes 0.000000
## Hhold.fctrSKn 0.000000
## Q98869.fctrNo 0.000000
## Q111220.fctrNo 0.000000
## Q105655.fctrYes 0.000000
## YOB.Age.fctr^6 0.000000
## YOB.Age.dff NA
## Final.RFE.X.Inc..rcv.bagEarth.imp
## Q109244.fctrYes 100.00000
## Q109244.fctrNo:Q113181.fctrYes NA
## Q115611.fctrYes 68.72166
## Q98197.fctrNo 53.76112
## Q109244.fctrNo:Q119650.fctrGiving NA
## Q116881.fctrHappy 0.00000
## Q109244.fctrNo:Income.fctr.L NA
## Hhold.fctrPKn 42.68977
## Q106997.fctrGr 0.00000
## Q108342.fctrOnline 0.00000
## Edn.fctr^7 16.12178
## Q109244.fctrYes:Income.fctr.L NA
## Q120379.fctrNo 0.00000
## Q109244.fctrNo:Income.fctr.C NA
## Q109244.fctrNo:Edn.fctr.L NA
## Q109244.fctrYes:Q116881.fctrHappy NA
## Q122771.fctrPt 0.00000
## Q100689.fctrYes 0.00000
## Q123621.fctrYes 0.00000
## Q109244.fctrYes:Q123621.fctrYes NA
## Q121011.fctrNo 0.00000
## Q101163.fctrDad 0.00000
## Q109244.fctrYes:Q101163.fctrMom NA
## Q109244.fctrNo:Q119334.fctrYes NA
## Q124122.fctrYes 0.00000
## Q121699.fctrYes 0.00000
## Q122771.fctrPc 0.00000
## Q104996.fctrYes 0.00000
## YOB.Age.fctr(35,40]:YOB.Age.dff NA
## Q108343.fctrYes 0.00000
## Q102674.fctrYes 11.34467
## YOB.Age.fctr(40,50]:YOB.Age.dff NA
## YOB.Age.fctr.L 21.38395
## Q109244.fctrNo 57.50965
## Q118232.fctrId 49.35879
## Q116881.fctrRight 45.48245
## Q101163.fctrMom 40.41641
## Income.fctr.L 38.45553
## Q108855.fctrYes! 36.28623
## Q120379.fctrYes 34.77541
## Q120472.fctrScience 32.69532
## Q115390.fctrYes 31.05100
## Q99480.fctrYes 28.72806
## Q116953.fctrNo 28.23229
## Q106993.fctrYes 25.88344
## Hhold.fctrSKn 24.50148
## Q98869.fctrNo 21.09239
## Q111220.fctrNo 18.57802
## Q105655.fctrYes 14.31542
## YOB.Age.fctr^6 12.10562
## YOB.Age.dff 22.80470## [1] "glbObsNew prediction stats:"##
## D R
## 1099 293## label step_major step_minor label_minor bgn end
## 6 predict.data.new 3 0 0 804.754 844.253
## 7 display.session.info 4 0 0 844.253 NA
## elapsed
## 6 39.499
## 7 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn end
## 4 fit.data.training 2 0 0 316.218 759.833
## 1 fit.models_1 1 0 0 6.813 261.688
## 2 fit.models 1 1 1 261.688 311.903
## 5 fit.data.training 2 1 1 759.834 804.753
## 6 predict.data.new 3 0 0 804.754 844.253
## 3 fit.models 1 2 2 311.904 316.217
## elapsed duration
## 4 443.616 443.615
## 1 254.875 254.875
## 2 50.215 50.215
## 5 44.920 44.919
## 6 39.499 39.499
## 3 4.313 4.313
## [1] "Total Elapsed Time: 844.253 secs"